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Making the Internet faster at Netflix

Making the Internet faster at Netflix

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Arbaz Nadeem
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June 26, 2020
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In our fourth episode of Breaking404, we caught up with Sergey Fedorov, Director of Engineering, Netflix to understand how one of the world’s biggest and most famous Over-The-Top (OTT) media service provider, Netflix, handles its content delivery and network acceleration to provide uninterrupted services to its users globally.

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Sachin: Hello everyone and welcome to the 04th episode of Breaking 404, a podcast by HackerEarth for all engineering enthusiasts and professionals to learn from top influencers in the tech world. This is your host Sachin and today I have with me Sergey Fedorov, The Director of Engineering at Netflix. As you all know, Netflix is a media services provider and a production company that most of us have been binge-watching content on for while now. Welcome, Sergey! We’re delighted to have you as a guest on our podcast today.

Sergey: Thanks for having me, Sachin!

Sachin: So to begin with, can you tell the audience a little bit about yourself, a quick introduction about what’s been your professional journey over the years?

Sergey: Yeah, sure. So originally I’m from Russia, from the city of Nizhny Novgorod, which is more of a province town, not very well known. And that’s where I got my education. I went to college from a very good, but also not very well known university and that’s where I had my first dream team back in 2009 when I was in third grade in college. I teamed up with my friends and some super-smart folks to compete in a competition by Microsoft, which is a kind of student contest where you go and create software products. In that year we were supposed to solve one of the big United Nations problems and what we did, we were building a system to monitor and contain the spread of pandemic diseases. Hopefully, that sounds familiar, but it’s what it was in 2009. And as a result, we had unexpected and very exciting success. We happen to take second place in the worldwide competition in the final in Egypt. And that was really exciting to be near the top amongst the 300,000 competing students. And it was really the first pivotal point in my career which really opened the world to me because the internship at Intel quickly followed and it was kind of the R & D scoped, focused on computer graphics and distributed computing. And a year after I was lucky to be one of the few students from Europe to fly, to Redmond, to be a summer intern at Microsoft. It followed with a full-time offer to relocate to the US upon graduation from college in 2011. At Microsoft, I worked in the Bing team helping to scale and optimize the developer ecosystem, particularly the massive continuous deployment and build system for the Bing product that Microsoft. That was a really exciting journey, but the relatively short one, because quickly after an unexpected, the referral happened to me with an invitation to interview for the content delivery team at Netflix, that was just kind of getting started and to help them build the platform and to link and services for the content delivery infrastructure. And quite frankly, I don’t expect that I’ll make it, but I couldn’t pass the opportunity at least to interview. But somehow I made it, very early in my career. I was 23 years old with just a few years of practical experience and it was quite stressful to join the company. I was on an H1B visa. I lacked confidence. I lacked a lot of, kind of relevant to and can experience in that area. Yet I gave it a shot, and I joined a team of world-renowned experts in internet delivery. And, um, I stayed there ever since. I will say that that decision and that risk that I took was the second big milestone in my career. Because from there it allowed me to grow extremely quickly and it allowed me to be truly on the frontier of technology and shape my mindset working for one of the top kinds of leading companies in the Silicon Valley, I’ve been here for about eight years. I initialized, I stayed on the platform and tooling side. I built a monitoring system, a number of data analysis tools. The overall mission of the team is to build the content delivery infrastructure, to support the streaming for Netflix. And over time, we added some extra services on top of pure video delivery. And a few years ago, that’s the group that I joined still staying within the same org, working on some of their extra advanced CDN like functionality, specifically developing some of the ways to accelerate the network interactions between clients and the server, uh, helping to better balance the network traffic, the traffic between clients and the multiple regions in the cloud. And I also worked a little bit on the public-facing tool. So I built the speed task called fast.com, which is one of the most popular internet testing services today powered by open connect CDN. And as of today, I’m a hands-on engineering leader. I don’t really manage the team. Instead, I work extremely cross-functionally with partners and folks across the Netflix engineering group. And I help to kind of drive major engineering initiatives in areas related to client-server network interactions. And I have to improve and evolve different bits and pieces of Netflix infrastructure stack.

Sachin: Thanks so much for that and it’s an amazing journey. You know, it’s really inspiring to see. Um, would it be fair to say that, you know, you kind of didn’t, it’s been serendipitous for you in some sense, did you plan to be here in the US and you know, be working in an organization like this or it all just happened back when in school, when you decided to participate in the Imagine cup challenge?

Sergey: Well, I wouldn’t say that I didn’t want to do that, but I definitely didn’t expect to, and I definitely didn’t expect to be in a place where I am today. I would say that my whole career was a very unexpected sequence of very fortunate events. I guess, in any case, I was sort of seeking those opportunities and I was not afraid to take a risk and jump on them.

Sachin: Yeah, that’s super inspiring for our audience and, like you correctly said, you got to seek those opportunities, and of course you need a little bit of luck, but if you’re willing to take those risks, doors do open. So, definitely very inspiring. Uh, so a fun question for you. What was the first programming language you, you ever recorded in and you still use that?

Sergey: Yeah, that’s a really interesting question. Um, the first language that I used was Pascal. And, uh, it was when I was 14 years old. So I started my journey with computers relatively late. And so it was kind of in the high school at this point. And the first lines of code that I wrote were actually on paper and I was attending The Sunday boot camp, led by one of the tutors who was preparing some of the folks to compete with ACM style competitions, where you compete on different algorithmic challenges. And he did it for free just for folks to come in. And someone mentioned that to me. I was like, Ooh, that’s interesting. Let me see what it’s about. And for the first few months, I was just doing things like discussing different bits and pieces about programming and all I had was a paper to write different things on. Later on, I of course had a computer and the first few years of Pascal was the primary entry for me to programming. And it was primarily around CLI and some of the algorithmic challenges. It’s only a couple of years ago when I discovered the ID and the graphic interfaces, and it really opened the world of what they could do. Uh, so yeah for me the first programming language is Pascal. And no, I don’t use it, but still have very warm memories of that because I think it’s a really, really good language to start with.

Sachin: Writing your first piece of code on paper. That’s an amazing thing. The folks who are getting into computer science today, they get all these IDEs, autocomplete, you know, all the infrastructure right upfront. Uh, but I think there is some merit in doing things the hard way. It prepares you for challenges and that’s my personal opinion.

Sergey: Yeah, I definitely agree with that. I’m not sure whether the fact that they had to go through that is an advantage or disadvantage for me, because I really had to understand the very basics and fundamentals. And I was super lucky with a tutor for that. He really didn’t go to the advanced concepts until I really nailed down the fundamentals. And I think having to really painfully go through that, if you’re kind of using a pen and sheets of paper, I think it really forces you to really get it.

Sachin: Right. Makes sense. So Netflix is one of the companies that has been growing massively over the last few years and acquiring millions of users. What are some of those key design and architecture philosophies that engineers at Netflix follow to handle such a scale in terms of network acceleration, as well as content delivery?

Sergey: Yeah, that’s an excellent question. In my case, as I mentioned, I’ve been here for quite a while and I had a lot of fun and enjoyed watching Netflix grow and be part of the amazing engineering teams behind it. But quite frankly, it’s really hard for me to summarize the base concept like use cases, there are so many different aspects of Netflix engineering and challenges, and that there are so many different, amazing things that have happened. So I’ll probably focus a little bit more on some of the bits and pieces that I had on the opportunity to touch. And for me, the big part of the success of growth was actually a step above the pure engineering architecture. It’s firstly rooted in the engineering culture because the first Netflix employees are great people. But second and most importantly, it really enables them to do the best work and gives them a lot of opportunities and freedom to do so. And with that empowerment and freedom to implement the best and to do the best work, I think the engineers are truly opening themselves up for the best possible solutions that really advance the whole architecture and the whole kind of service domain. On the technical side, in my experience, what I think was fundamental to effectively scale infrastructure is the balance that we have had between innovation and risk. And in our case, many fundamental components of our engineering infrastructure are designed to be extremely resilient to different failures and to reduce the blast radius, to contain the scope of different issues and errors. With that’s really embedded like this thinking about errors, thinking about failures, it’s really embedded in the mindset and that leads some of the solutions and some of the implementations to be really robust and really resilient to some of the huge challenges and lots of unexpected demands. And in that aspect is that many systems I designed and thought of to scale 10 X from the current state. So that’s often when we think about the design, we don’t think about today. We think about the 10 X scalability challenge, and that includes both architecture discussions and some of the practical things like performing the skill exercises constantly and stress testing our system, both existing and proposed solutions and constantly making sure that things can scale. So in case, we have unexpected growth, we have confidence that we can manage it. And I think as a result of that, we are not only getting an architecture, that’s stable and scalable. But we also get an architecture that’s safe to innovate on, because we can do the changes with more confidence that we can roll back things. We have confidence in our testing and tooling and with that confidence, I think it’s much as much easier to apply and do your best.

Sachin: Interesting. So you spoke about designing for innovation as well as being resilient and then kind of designing for a 10X scale in the very beginning. So typically, and this is my experience and I may be wrong here, but when we were younger in our journey as a software engineer, right, we tend to get biased towards building out the solution very quickly and, do not have that discipline to kind of think about the long term scale and all of those challenges, because that is very deliberately put that in place. Right. So, so has there, like, how did your journey kind of evolve in that? Are there any tools, techniques that you use to kind of force yourself to come up with the right architecture? Could you talk a little bit about that?

Sergey: Well, so I think you were what you touched upon a really great point, but it’s, I would say it’s a slightly different dimension, a bit more of a trade-off between the pace of innovation and sort of the technical debt, the quality of code, so to speak. And I think this is an extremely broad topic, uh, with where I would say their answer would really depend on their application domain. For example, I would give you one answer if you were working on some medical or military services, versus some ways like a social network, consumer and product entertainment sort of services because the risk of failure and the mistake is completely different in that case. And I think another factor comes from the understanding of the problem. There is, I think, a big difference in designing the system for the problem that you understand really well, and you have a pretty good idea that it’s there to stay for quite a while versus more of an exploration where you’re not exactly sure whether this would work or not. You are still trying to kind of get a hand at it. And, uh, quite often you start with a second, with a latter option, and that’s what made you start to do. And I would say that in that case, uh, in my personal experience, I think it’s much more productive to focus on the piece of innovation. And, uh, maybe in some cases build some of the technical debts, maybe in some cases to compromise some of the aspects of the best practices but being able to get things out and get some kind of bits and pieces really quickly and learn from it. And since you are relatively lightweight, it’s much easier to pivot and change direction. At the same time, it doesn’t mean that we all have to be Cowboys and break things here and there. There is a balanced approach. You can still invest in the core principles and the core architecture that allows all those things innovations to happen safely. And I think at Netflix, that’s what really we excelled at. We have some of the core components, some of the core tools that are available for most of the engineers. That’s allowed to make things, uh, and innovate safely while not being overly burdened by some of the hard rules and, uh, some of the complicated principles and gain that experience. And I would say this is sort of a natural process. You have something that’s done relatively quickly. Then you were at this kind of crossroads. Whether now you know, this is a real thing and you’ll have to scale it. And then you would likely apply a different way of thinking or maybe it doesn’t work and well you save a bunch of work by not overcommitting to something really big before confirming that this is useful. And at this point when you were on the road to actually build it for the long term, it might be the proper solution to rebuild what you’ve designed in the past. And it might sound like you were wasting a lot of time. Like you’re doing the double effort. But the way I see it, there’s actually, you’ve saved a lot of time because you were able to relatively cheaply test a bunch of lightweight solutions. You got the confidence, what really works. And now you’re only investing a lot of resources on building the long term for the one thing, and essentially you’ve saved all the time by not doing that for all other ideas that you’ve had. Um, I have them all, it’s sort of a 20, 80 rule that takes 20% of the time to build a working prototype and it takes 80% of the time to productize that and make it resilient and scalable. Um, in many aspects of innovation, it makes sense to start with the 20 and only go for the 80% over time. Yeah, but as I mentioned, it doesn’t mean that everything has to be all or nothing. There are still major principles and it definitely makes sense, especially as you get larger to invest in the main building blocks to enable those things to happen safely. There are always some of the quantum principles that are cheaper and easier to follow in all scenarios. I think one of my favorite books that I was lucky to read early on is the Code Complete by Steve McConnell, which goes into the lots of fundamentals about just writing good and maintainable code, which in most cases doesn’t take more time to write. I just need to follow some relatively simple guidelines.

Sachin: Gotcha. That’s a very interesting perspective. If I were to summarize it, you were saying that, uh, architecture design is context-dependent. You got to know what the problem is and what you’re optimizing for. And sometimes you’ll go for something lightweight and then optimize it later on because the speed of innovation is also important, but there are always certain principles that one can use without really increasing the development time, certain strong arteries that can help in building robust code. So that’s, you know, definitely interesting. Uh, another fun question. Do you get time to watch any shows, movies on Netflix, and if so, which one’s your personal favorite?

Sergey: Yeah. Well, while often I don’t have a ton of time to watch I definitely love to have an opportunity to relax and enjoy a good show and Netflix is naturally my go-to place for doing that. And, I’m in a losing battle to keep up with all the great shows that I would like to watch. And, um, it’s quite hard for me to choose one favorite. So I think I’ll cheat and I’ll choose a few instead of just one. So I hope you’re fine with that. I think one thing is I’m a fan of sci-fi as a genre and I really enjoyed Altered Carbon, especially the first season. And over-time I’m also learning that I’m affectionately a fan of bigger shows that I have no idea about. And the one title that I really enjoyed was ‘The End of the F***in world’, which is a dark comedy-drama. It follows the adventures of two teenagers. It’s a really kind of unique piece of content and I truly enjoyed every episode of it. I’m really glad that as a company, we really invest in more and more international content, not just coming from the American or the British world. And the latest favorite for me was ‘The Unorthodox’, which is a German American show with most of the dialogues actually in Yiddish, which is a part of the Orthodox Jewish culture. I enjoyed both the personal story and I also learned a lot about it because I had no idea about this part of the cultural experience for some of the folks. I was both enjoying the ways, done the story behind it, and it had a huge educational component.

Sachin: Thanks for sharing that. So moving back to the technical discussion. So you worked at multiple organizations, you know, Intel, Microsoft, while having the bulk of your time you have spent at Netflix. If you were to look back and think about one or two major technical challenges that you faced and is there something that you would like to talk about and more so along the line of how did you overcome it?

Sergey: Sure. So I think I’ll probably choose one of my favorites. And I think that’s the biggest challenge that I can recall probably by far. And that was my first major project when I joined Netflix. So the task was to build the monitoring seal system for the new CDN infrastructure. And, that was really quick as the task quickly forwards after I joined the CDN group at Netflix. As I mentioned, I was relatively early in my career. I was relatively inexperienced. I know very little about this domain and there’s a huge infrastructure that’s about to like, is being built and we are migrating a lot of video traffic on it. And this is a huge amount of traffic. At that point, Netflix was about one-third of all downstream traffic in North America. So like a third of the internet is there. And here I am like a new employee, that’s not like, Hey, let’s go see some that will tell us how we do like that. We’ll monitor the main state of the system. Like you will, you’ll have to design the main metrics. And really design the system end-to-end on both the backend and the front end, that of UI. And in the true Netflix culture was given the full authority to make its own tactical decisions on product design and implementation. So it was just a full-on like, here’s the problem context, please go and figure it out and we are sure you’re, you’re going to agree. And The biggest challenge of all of that is that many aspects of the system were new and quite unique. And even the folks who were working on this history for a long time, they were quite upfront that we are learning as we go in many ways. So we cannot really give you the precise technical requirements, but we actually wanted to look at. And overall we wanted to keep the whole system and the approach to the monitoring as hands-off as possible, just to make sure that the system reflects some of the architectural components, which reflect some of those principles like a self-healing system that’s resilient to individual failures. So I had to fully understand the engineering solution. I had to model it and there, in terms of the services and the kind of data layer. I had to look at and partner really closely with the operations team to learn a lot about how the system performs, what metrics we should look at, what’s noisy, what’s not. And it’s been quite a ride but especially remembering that was an extremely fun challenge. And I think some of the things that were fun like: a) That I was very unexpected, given the huge responsibility on a pretty critical piece of Netflix infrastructure stack and I was given full control of what I’m using for that. And I could either choose something that I’m comfortable with or something that’s completely new to me. There were really fun interactions with various folks, even though some of my teammates were not necessarily experts in building cloud services or building UIs. There were many other folks at the company who were extremely open and helpful to get me up to speed. I think some of the things that have allowed me to where success is that system is still used today with lots of components still the same as they were built many years ago. I think I made the right decision to focus on very quick iteration. As a matter of fact, the first version of the system fully ready for production and actually used by the on-call by the operations team was done in about two months. And that with me learning how to deploy ADA services in the cloud. I chose Python as a framework, and I knew very little about it before I learned the new UI framework and kind of built the front end in the browser for it. But focusing on the initial core critical components and getting something working was a huge help because it allowed me to build a full feedback loop with the users and started to start learning about the system. And then that calibration of the stakeholders allowed it to iteratively evolve it over time. And even though I didn’t know a lot of different things early on, I was extremely flexible and adaptable. I think some of the key things that were critical for my success to get it done is my ability to wear my mistakes, to be very upfront about mistakes, and actively seek help. And I think that’s one thing that I often notice, different people are not doing for various reasons. They think that it’s not the key to make mistakes, or they are somewhat unskilled or unqualified if they ask for help. For me, it’s been always the opposite. No one, nobody knows everything. Nobody’s perfect. Everyone, everyone makes mistakes. And, uh, the sooner you realize it and the more upfront and open you are around those aspects. The better you’ll be able to find the ideal solution and the faster you’ll be able to learn over time.

Sachin: Right. So it would have been a lot of confidence for you back in that time. Like you said, you were early in your career and the organization just said, Hey, this is your project. You have complete authority to just go out and do. And when we know, we’re sure you do the right thing, it must have also given you a lot of confidence, right?

Sergey: Well, quite honestly, initially it didn’t. Initially, it freaked me out because I was especially after companies like Intel or Microsoft, where their approach is very different. And I only had a few years of experience and I was not a well-known expert. That was very unusual. It was very scary. I would say the confidence really came months later when I was starting to see that the key is something that’s been built, that’s been used, I’m getting good feedback. And people are thanking me for working on that. They are giving some constructive feedback. They make suggestions, and I’m becoming the person who actually knows how to do it. Then in some of the domains, I’m becoming the most knowledgeable person, which is natural when you’ve worked on that. I would say confidence really came at this point, which was many months after that I would say probably a year or so. Maybe even after that.

Sachin: Got it. That makes sense. So, moving on to the next question, do you believe engineers should be specialists or generalists and how does this really impact career growth in the mid to long term?

Sergey: Yeah, that’s a great question. And personally, I don’t think there is one right style. To me, it’s like comparing what is more important, front end or backend. I think any effective team requires both types of personalities. And for nearly any major project, you need to rely on those because if you think about it, if you have a team of only specialists, you’ll have really well done individual pieces of the system, but it will be really hard to connect them together. Similarly, if you only have generalists, you may have liked a lot of breaths, but it would be really hard to actually build truly innovative aspects of the products because that’s the point of focusing on the one area that you have to give a compromise and not know something else. I think ultimately for effective teams, you need both times and you really need to have effective and efficient communication between both groups of them. You need them to be able to work together as a very well-aligned team. Uh, so yeah, I think for me personally, like what type of engineer to be is more of a personal choice. And also in my experience, there have been many opportunities to change the preference. You don’t have to necessarily pick ones and stick to that. You can mix it as you can go into one area or another. In my case I’ve been a specialist at some point and actually in the early stages of my career, I was probably the most specialized. When I was at Intel, it was a heavily dedicated area focused on computer graphics. I was optimizing some of the retracing algorithms and methodologies, what specific types of the network of Intel hardware. So it was all of low-level C, assembly, and some of the specific Intel instructions for, to get the most out of it. At Microsoft, I worked on search and some of the developer experience, then I switched to network and networking. So it’s, it’s sort of a mix. So I think I was becoming more of a generalist over time. On the tactical stuff, but still, I’m specializing in which area on the larger area. But this is also a personal choice and the industry and the technology is moving so fast that even if you were the expert in one area, very specialized today, in fact, years, you might, if you’re not keeping up, you might be off-site or that area is not everything. And you don’t have to stay there. You may find the passion somewhere else and switch to it. Or you can always stay as a generalist and just explore and move alongside technology growth.

Sachin: Yeah. So if I, if I were to summarize that, uh, you’re saying teams eventually need both kinds of engineers, and it really boils down to a personal choice, whether you want to be a specialist or a generalist, but, you know, given the current pace at which like you said, technology is evolving, it’s really hard to just be narrow jacketed into one thing, you know, because things around you would just constantly change and then you’ll have to adapt to them.

Sergey: Well, I think it’s on the latter point, I would say, I would say really depends. There are some of the areas that remain relevant, uh, for quite a while, for example, talking about the networking area, we’re still using TCP and that’s the technology from the 1980s. And there is still a lot of really interesting research and developments going on. And if anything, in recent times, the pace of development has accelerated. And yet, someone who specialized in that in the nineties would be still very relevant today. So in some of the areas you can still, you can specialize and you’ll be growing your influence. You’re growing your impact over time, but there’s no guarantee and it’s really hard to predict those areas. So I think, well, if you’re really passionate about it, it makes sense to stay. But I would say you should always be ready to pivot go and dig into something else.

Sachin: That makes sense. So another fun question, which software framework or tool do you admire the most?

Sergey: I think my answer will be probably quite boring at that. I’m pragmatic, I don’t have a favorite intentionally. I tend to follow the principle that there is always the right tool for the job. And as that principal and trying to avoid any sort of absolute beliefs or absolute favorites. Having said that, uh, the very few frameworks that I personally like and they’ve helped me quite a bit. I like Python quite a bit for its simplicity, its flexibility. From personal experience, it’s one language I was able to deliver a fully usable work in projects that are being consistently used for several years after in just two weeks. And before those two weeks, I barely knew Python. So I think that shows the extreme power of the language, how easy it is to pick up and do something actually practically useful. Related to Python, I like pandas quite a bit, which is a statistical library with some of the ways to do time serious or data frame analysis. From the network world, I should mention Wireshark, which is a general tool and it’s fantastic and definitely go-to for me to understand all that happens on the network communications at an insane level of detail. In terms of overall impact, I should mention the Hive, which is a big data framework. While it’s becoming sort of obsolete technology right now replaced by Spark and all of the following innovations. I think it’s really created a revolution in many ways. In its own time, creating, making it possible to access enormous amounts of data, very easily using the very familiar SQL like language. And for me, I happen to use it around the time and it really had a massive impact on a number of insights into things I was able to do.

Sachin: Interesting. I agree with you on the Python bit. I myself learned Python very quickly and saw the power of the framework and the versatility in terms of the things that allow you to do, like there’s hardly any industry domain, where, where you can’t use Python to very quickly prototype. Right? So in that sense, it’s a very powerful and versatile framework. Thanks for that. Let’s move on to the next one. You know, given the current scenario around COVID-19 everybody working from home, what’s your take on remote engineering teams? Personally, what do you feel about remote work and you mentioned that your work involves a lot of cross-team collaboration? So how has that been impacted positively or negatively in recent months?

Sergey: Yeah, so I think for the first question for remote work in general, the group that I’m in the content delivery group at Netflix, we were remote from the ground up. So our teammates, they are all scattered around the globe all the way from Latin America, to the US, to Europe, to Asia and all the way to Australia. In terms of working remotely we’ve figured out the way to do it very efficiently, but what’s challenging is that now we are a hundred percent remote because what you’ve done in the past, like some of the folks that are in the office, like in Los Gatos in California, some of the folks that are working from home and we effectively collaborate with each other, but every quarter we will do what we call the group of sites where everyone would get together in the same place. We will have a number of meetings and discussions, both formal and informal, where you’ll be able to sort of put the actual person to their image that you see on the screen. And you’ll be able to really know those persons, those folks, your teammates outside of their direct work domain. In my experience, that’s hugely impactful in terms of affecting your future interactions and building a relationship and working together as efficiently as possible. And with today’s COVID-19 world, we are losing that. So we are 100% remote and even though it hasn’t been a hugely long period of time, based on some estimates, it might take a while for us to work the way. And, it’s a challenge not to have some of that context and to lose some of this nonverbal thesis of communication. To your question, it’s also much harder to build new relationships. I would say it’s still possible to sustain some of the relationships that you’ve built from the past based on previous work together, previous interactions. But when you have to meet a new partner or when there is a new person joining the team, it’s extremely hard to find the common commonalities or find the same language, when you only have a chance to interact via chat or VC. I would say we are definitely trying different things to fix that. We haven’t found the perfect solution. We hope to find it. I would say we also call that you won’t have to find it for the longterm. Hopefully, the COVID-19 situation will be addressed as quickly as possible. But yeah, that’s the very few things that I would say that’s becoming even more critical. First is extremely clear and efficient communication. It becomes paramount and the sharing of the context, and especially from the leadership side, it becomes extremely important to make sure that everyone is on the same page. And that you really need to double down on all of the context sharing in that sense. And, uh, in terms of the partners, I think it’s extremely important to make sure that folks feel safe when they work that way. Because as part of not having a chance to talk face to face, it’s a great environment too, uh, for some sort of or kind of fear and paranoia to build up. Um, it’s harder to make sure like how you’re doing, how things are going, especially when there’s lots of stress happening on the personal side as well and there is lots of research that shows that we are not productive when we are experiencing high levels of stress. And, uh, I would say that’s on the individual side. It’s really critical to make sure that both yourself and all the partners around you are feeling safe and in the right state of mind primarily. And then it comes down to where something that’s really difficult, which is building trust between each other to do the best work. Even in the case, when you are very far away from each other, you really need to make sure that once you share it’s all the context about the problems, about the solutions, about the ideas. You have the full trust in others to do the best work to address some of the things and help you with some of the things or ask you for help as well.

Sachin: Got it. That makes sense. I completely agree with you on the fact that. Having a shared conversation in person is definitely different from having it over video and the kind of relationships that get built subconsciously is very, very hard to replicate that on video and, and I’m with you that hopefully, we can safely return back to work at some point in time sooner, rather than later.

Sergey: In the meantime, but one sort of thing that we are doing is that we are making sure that we still communicate informally. One thing that we do as a team, we have three times a week, we have a virtual breakfast. If someone can’t make it that’s okay. But otherwise, folks just have an informal breakfast together. And we tried to talk about things unrelated to work, uh, just any subject, basically something that you would have as a conversation if you went for the team lunch outside.

Sachin: That’s interesting. And is that working out well, like, do you see people interacting and joining these discussions?

Sergey: In my opinion, yes. I think personally I feel much more connected after those things. When I have an opportunity to hear and see folks discussing aspects outside of the specific tactical work domain. I think it’s useful for others. It’s good for morality. And I’m seeing that many other teams experimenting with different ideas along the same lines.

Sachin: Nice. So, onto the next question, you know the tech interview process is talked about a lot. People have their different opinions. What’s your take on given the current norms around tech assessments and interviews? What do you think is unoptimized today or what in your opinion should be changed?

Sergey: Cool. Would you mind clarifying, are you asking specifically about the current, highly remote situation or interviewing in general?

Sachin: Tech interviewing in general, the process that, you know, that is there. I’m assuming Netflix, other than the cultural aspects, maybe from a talking perspective and your previous organizations have had similar methods or processes. So do you think there’s something that we could do better? Not in the context of COVID-19 per se, but in general.

Sergey: All right, got it. I think it’s generally, I think there are lots of challenges with a typical interview process. And if you think about it, the typical interview experience where we have someone coming in for 30-40 minutes, solving some of the specific problems on the whiteboard, or sometimes on the shared screen, it’s not exactly what we experience in the day to day life. Quite often the problems are not very well defined, but you very rarely have specific constraints on time to solve it. Most of the time or I hope almost all of the time, there is much less stress in the typical work environment and you’re relating the person to something that they might not have the subtle experience in the workplace. At Netflix, many teams do try different – different approaches. We don’t have a single right way that everyone has to follow. Depending on the team, depending on the application domain, often depending on the candidate, folks will try to adjust the interview process. In our case, what we have tried and what we genuinely try to do, we’re avoiding very typical whiteboard questions. We try to focus on some of the problems that are much closer to real life. We try to lean on some of the homework, take-home assessments if possible. If the candidate has time to perform that and a general, I think this gives a much better read of the candidate skills because they can take it in the environment that they’re used to. There is no stress. There is not someone looking over the shoulder. And you can assess a much broader range of skills, not just a specific, like, I know how to solve it the way I don’t know how to solve it, but how do you write code? How do you document that? How do you structure it? And in some cases like even how do you deploy it? And those operational aspects of coding is a big part of engineering life, which are extremely important to assess as well. And I would say generally it’s a huge benefit if a candidate has something to share in the open-source and the open environment. If they have a project that someone can just follow or can take a look at the code, I would say that’s one of the best assessments of the skills it has just working, that’s been used, and that has been produced. It still doesn’t cover all aspects of it. It’s really hard to assess the qualities like teamwork or some of the compatibilities with the teammates. Um, those areas tend to be quite freaky. Um, and honestly, I don’t think I have any ideal solutions for that other than to make sure that as many partners for the new hire as possible are actively participating in the interview process. They have the ability to chat a little bit more and get an idea of whether they can work with a specific person and achieve strategies to do that depending on the team size or particular situation.

Sachin: Got it. So if I were to summarize this, if the interviewing process can be as much as possible, close to the actual work that you’ll be doing, while eliminating or reducing the stress that one goes through in the interview process, that should bring out a more fair assessment of the candidate.

Sergey: I would say, yeah, at least that’s the general strategy that in my experience, in the interview processes, I tend to follow.

Sachin: Interesting. So, another fun question, if not engineering, what alternate profession you would have seen yourself excel in?

Sergey: I would say it really depends on the time when you would ask me. I happen to get excited very easily and my immediate passions change quite frequently. As of recently, I would say I could easily find myself having a microbrewery or running like a barbecue-style restaurant. So those are the two things that I found interesting and I’m doing quite consistently for the last few years. I homebrew in my garage. I also have a few kegs of homebrew on top. And I have three grills in my backyard and those things complement each other very nicely and they bring lots of joy to myself and my friends as well.

Sachin: That’s really nice to know that you have a home brewery and you said you’ve been doing it for two years now.

Sergey: Uh, well, I would say more about five years.

Sachin: That’s an interesting hobby. Uh, so, you know, with that we are almost towards the end of our podcast. The final question today: So if there was like one tip that you could give to your peers, people who are at a similar role and even to those people who want to step up and, you know, come to a role where you are today, what would that be?

Sergey: I think I would respond with sort of a catchy phrase from our Netflix culture deck. And I think that defines the leadership style that the company tends to follow and that I personally strive for, which is leading with context and not control. And what that means is that as a leader, learning to gather, summarize, and effectively communicate the most critical goals and challenges that the business, you, your group faces and effectively share it with the team but trust the individual contributors and your partners to find the most optimal solution and execute it and not trying to do both at the same time, which is really hard to do it, but that’s, that’s what often happens. Because I think that empowering the folks with the proper knowledge and the kind of context around the problem, encourages folks to fully own it and better understand it and they become much more committed to that. And that has a much higher chance to provide the best optimal solution versus the situation when someone just tells you what to do like ABC. And that you’ll get more commitments. I think it inspires folks to grow much more. And I think overall it makes the person who is able to foster such an environment a much better leader, which is also extremely challenging to do. You’ve asked me for advice like for the managers, directors. I’m not sure I’m qualified to give that advice. Uh, it’s more of some things that I’m working on to prove myself and, as someone who is relatively new to their engineering leadership role, I’m finding lots of challenges and struggles, and also those things where you feel like, uh, you might know various aspects of the solution, but you don’t really have to be actively involved in every bits and piece of it and balancing those things is a huge challenge. And personally, as I progress on those, I see that I’m becoming more efficient and more useful for the group and for the company. And I think it’s a kind of ideal and useful goal to live by.

Sachin: So it’s more about empowering people so that they can find their own solutions. And then certain times you may even have the right solution in your hand, but you don’t want to do it because you want the people to fight their own battles. And maybe they come up with something completely different that you might not have imagined. So fostering that innovation is important.

Sergey: Yeah. I would say empowering with the context around the solution and empowering down with the trust for them to execute on it and fully own the implementation.

Sachin: Makes so much sense. And I think you’ve gone through the same in your journey at Netflix. From the early days, you got the context and you got full control.

Sergey: Absolutely. Yes, I experienced that and the full power of it as an individual contributor. And now I’m actively trying to get better at doing that for others as well.

Sachin: Yep. That makes sense. Sergey, it was a pleasure having you today as part of this episode, I really appreciate you taking your time. It was informative and insightful, and I definitely enjoyed listening. I hope our listeners also have a great time listening to you.

Sergey: Thanks a lot, Sachin! session. It’s been a pleasure to have a chance to share my story.

Sachin: Thank you. So, this brings us to the end of today’s episode of Breaking 404. Stay tuned for more such awesome enlightening episodes. Don’t forget to subscribe to our channel ‘Breaking 404 by HackerEarth’ on Itunes, Spotify, Google Podcasts, SoundCloud and TuneIn. This is Sachin, your host signing off until next time. Thank you so much, everyone!

About Sergey Fedorov
Sergey Fedorov is a hands-on engineering leader at Netflix. After working on computer graphics at Intel, and developer tools at Microsoft, he was an early engineer in the Open Connect — team that runs Netflix’s content delivery infrastructure delivering 13% of the world Internet traffic. Sergey spent years building monitoring and data analysis systems for video streaming and now focuses on improving interactive client-server communications to achieve better performance, reliability, and control over Netflix network traffic. He is also the author and maintainer of FAST.com — one of the most popular Internet speed tests. Sergey is a strong advocate of an observable approach to engineering and making data-driven decisions to improve and evolve end-to-end system architectures.

Sergey holds a BS and MS degrees from the Nizhny Novgorod State University in Russia.

Finding actionable signals in loosely controlled environments is what keeps Sergey awake, much better than caffeine. This might also explain why outside of work he can be seen playing ice hockey, brewing beer, or exploring exotic travel destinations (which are lately much closer to his home in Los Gatos, California, but nevertheless just as adventurous).

Links:
Twitter:@sfedov
Website:sfedov.com

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Best Diversity Recruiting Software for DEI Hiring in 2026

The Strategic Evolution of Inclusive Talent Acquisition in 2026

The recruitment landscape of 2026 has undergone a fundamental transformation, moving past the era of performative commitments toward a state of systemic, data-driven inclusion. As organizations navigate a "low-hire, low-fire" economic cycle, characterized by high competition for top-tier talent and increased caution in headcount expansion, the strategic value of an inclusive workforce has never been higher. Diversity, Equity, and Inclusion (DEI) are no longer managed as peripheral corporate social responsibility initiatives; instead, they have become the "operating system" of high-performance organizations. This shift is fueled by a growing realization that diversity is a hard economic asset that directly correlates with profitability, innovation, and long-term resilience.

By 2026, the global workforce expectations have solidified around transparency and authenticity. Candidates are increasingly skeptical of broad public statements and instead demand proof of an inclusive culture during the application process itself.1 Research indicates that 76% of candidates consider diversity a non-negotiable factor when evaluating job offers, and organizations that successfully foster a sense of belonging see 40% lower turnover rates. The "Quiet Commitment" trend highlights a transition where companies are stripping away flashy labels but deepening the actual work of equity, weaving it into the very bones of their hiring processes, from how feedback is gathered to how projects are staffed.

The role of technology in this evolution is paramount. Artificial Intelligence (AI) and specialized recruiting software have transitioned from being tools for efficiency to being the primary inhibitors of unconscious bias. In 2026, the question for HR leaders is no longer whether to use DEI tools, but how to govern them to ensure they are "inclusive-by-design". These platforms enable recruiters to manage complex regulatory requirements, such as the EU Pay Transparency Directive, while simultaneously auditing their own algorithms for historical bias. As boards and Chief Diversity Officers (CDOs) work closer together, the emphasis has shifted toward "Innovation Friction" , the healthy exchange of ideas that occurs in diverse teams and prevents the stagnation of groupthink.

Why Diversity Hiring Tools Matter in a Polarized Environment

The necessity for specialized diversity hiring tools in 2026 stems from the inherent limitations and biases of human judgment. Traditional recruitment methods, often reliant on "gut feelings," casual interviews, and prestige-based resume reviews, frequently inadvertently disadvantage candidates from underrepresented backgrounds. Diversity recruiting software provides the structural framework required to neutralize these biases, ensuring that every candidate is evaluated against objective, competency-based standards. This is particularly critical in 2026, as the definition of DEI has expanded to include neurodiversity, disability inclusion, and socio-economic geography.

The economic case for these tools is supported by rigorous data from global consultancies. Organizations in the top quartile for gender and ethnic diversity are consistently found to be 25% to 36% more likely to achieve above-average profitability than their more homogenous competitors. This performance gap is attributed to the fact that diverse teams solve complex problems faster and bring varied mental models to the table. However, representation alone is insufficient; inclusion must be built into the system to ensure that diverse talent feels safe enough to contribute, making psychological safety a key leadership metric for 2026.

Impact Area Business Outcome DEI Significance
Profitability 36% higher likelihood of outperforming peers. Diversity serves as a hard economic asset rather than just a moral goal.
Innovation 19% higher innovation revenue. Diverse management teams prevent groupthink and foster creativity.
Retention 40% lower turnover rates in inclusive teams. Belonging reduces attrition, which is a significant cost saver in a tight market.
Decision Making 87% better business decisions. Varied perspectives lead to more robust and comprehensive strategy.

Defining the Diversity Recruiting Software Ecosystem

In 2026, diversity recruiting software is categorized by the specific stage of the hiring funnel it addresses. These tools range from top-of-funnel sourcing engines that expand the reach of recruiters to bottom-of-funnel interview platforms that ensure objective evaluation. A critical trend in the 2026 market is the move away from "box-checking" toward a holistic workflow that enables humans through technology.

Sourcing and Pipeline Expansion

Sourcing tools are designed to surface candidates who might be invisible to traditional keyword-based searches or restricted professional networks. In 2026, these tools leverage massive, pre-compiled databases sometimes exceeding 1.2 billion profiles and aggregate data from diverse sources such as GitHub, Stack Overflow, and academic patent offices. These platforms allow recruiters to apply deep filters for demographic groups, including military veterans, LGBTQ+ individuals, and women in technical roles.15 By identifying "likely open" candidates through AI, these tools save significant time and help build a talent pipeline that is proactive rather than reactive.

Screening and Objective Assessment

Once candidates enter the pipeline, screening software removes the subjective triggers that lead to bias. This includes PII (Personally Identifiable Information) masking, which hides names, photos, and graduation dates to focus exclusively on skills. In 2026, "skills-first" hiring has become the industry standard, where candidates are evaluated through standardized coding challenges, logic tests, and gamified neuroscience assessments. These assessments provide a richer "talent signal" than a GPA or previous employer's brand name, allowing candidates with non-traditional backgrounds to demonstrate their high potential.

Language Optimization and Bias Interruption

The language used in job descriptions and outreach emails is often a significant barrier to diversity. Augmented writing tools use AI trained on millions of HR records to identify gendered phrasing, age-restrictive language, and subtle biases. In 2026, these tools provide real-time scores that predict how likely a job post is to attract underrepresented groups, essentially interrupting bias before the hiring process even begins.

Structured Interviewing and Conversational AI

The interview stage is often the most susceptible to "affinity bias," where interviewers favor candidates similar to themselves. Diversity interviewing tools mandate a structured approach, utilizing consistent scorecards and pre-defined question kits for every applicant. Additionally, conversational AI assistants and asynchronous video interviews provide flexibility for candidates, allowing them to engage at their own convenience and reducing the logistical hurdles that disproportionately affect minority candidates.

How to Choose the Right Diversity Hiring Tool for 2026

Selecting a DEI tool in 2026 requires a framework that prioritizes transparency, integration, and ethical AI governance. Organizations must move beyond feature lists and examine the "bones" of the technology to ensure it aligns with their strategic resilience goals.

Algorithmic Transparency and Governance

The most critical factor in 2026 is the vendor's commitment to algorithmic audits. Organizations must ask for proof that the software’s scoring logic is transparent and has been audited for adverse impact. Since hiring algorithms often learn from historical data that may be biased, it is essential that the software includes mechanisms for "bias-detection protocols" and clear rules for human review.2 A "black box" AI that ranks candidates without explainable criteria is a systemic risk that can reverse hard-won equity gains.

Integration and Workflow Seamlessness

A diversity tool that exists in a silo will eventually fail. In 2026, the best platforms integrate natively with existing Applicant Tracking Systems (ATS) and Human Resource Information Systems (HRIS). This ensures that inclusive processes do not create additional administrative burdens for recruiters. High-performing teams look for "bi-directional messaging" and "CRM-style" candidate nurturing that allows for a cohesive experience from sourcing to onboarding.

Candidate Experience and Accessibility

The candidate is the primary stakeholder of any recruiting software. In 2026, a "mobile-first" and "app-less" experience is mandatory to accommodate candidates across all socio-economic levels. Software should support name pronunciation recordings, pronoun selection, and multilingual assessments to ensure that language and identity do not become barriers to entry. Furthermore, for technical roles, the IDE must be accessible and provide practice environments to level the playing field for self-taught developers or those from underrepresented institutions.

Scalability and ROI Indicators

Organizations must evaluate whether a tool scales with their hiring volume. For enterprises processing tens of thousands of applications, automated pre-filtering and conversational AI are necessary infrastructure. For startups, lightweight solutions with transparent, pay-as-you-grow pricing models are more appropriate. The tool should also provide detailed analytics that link team composition to key performance indicators (KPIs) like innovation rate and innovation revenue.

Top Diversity Recruiting Tools for 2026: Comprehensive Comparison

The following tools represent the leaders in the 2026 DEI landscape. Each addresses a specific niche, from technical assessments to inclusive language.

Software Best For Key DEI Features Pricing Model
HackerEarth Technical Equity Blind hiring (PII masking), 10M developer community, hackathons, and 40+ programming languages. Growth: $99/mo; Scale: $399/mo; Enterprise: Custom.
Greenhouse Process Governance Structured interview scorecards, candidate name pronunciation, and pronouns. Quote-based; Starting around $6,000/year.
Textio Language Optimization Augmented writing, predictive diversity scores, gender/age meters, and bias interruption. Starting from $15,000/year for small teams.
SeekOut Precision Sourcing Diversity filters for Black, Hispanic, Asian, and Veterans; "Coder Score" for GitHub. Annual contracts $10k - $90k+ (Avg $27k).
HireVue Scalable Video AI Standardized video templates, psychometric games, and adverse impact testing Essentials starts at $35,000/year.
Pymetrics Behavioral Science Neuroscience games assessing cognitive/emotional traits without cultural bias. Custom quote-based pricing for enterprises.
Manatal Budget-Friendly AI Unbiased AI-driven candidate scoring and access to global underrepresented groups. From $15/user/month.

HackerEarth: Deep Dive into Technical Inclusion

HackerEarth is the industry standard for developer and technical hiring in 2026. Its primary strength is the neutralization of "pedigree bias" through the use of skill-based evaluations. By utilizing a vast library of 15,000+ challenges, companies can move beyond resumes and GPAs to focus on actual coding ability and problem-solving. The platform's blind hiring feature is particularly robust, allowing recruiters to mask all identifying information including names and college Hubs to ensure a pure merit-based evaluation.

For campus recruiting, HackerEarth provides a unique advantage by hosting global hackathons and coding competitions. This allows organizations to reach students at thousands of institutions, including HBCUs and Hispanic-serving institutions, breaking down the geographic barriers of traditional on-campus recruiting. The platform also supports five regional languages, ensuring that language proficiency does not unfairly disadvantage non-native English speakers in technical assessments.

Greenhouse: The Architect of Structured Hiring

Greenhouse excels in creating a repeatable and fair hiring process. Its "Structured Hiring" methodology is built to minimize unconscious bias by ensuring every candidate for a specific role is evaluated against the same criteria. In 2026, Greenhouse's integration ecosystem is its greatest asset, connecting with over 500 other HR tools to provide a comprehensive view of the hiring pipeline.

The platform’s DEI tracking tools allow teams to audit their entire funnel for demographic drop-offs. For instance, if data shows that women are dropping off significantly after the initial phone screen, Greenhouse provides the analytics needed to investigate the cause—whether it is biased interviewer feedback or a flaw in the role’s definition.

Textio: Predicting Inclusive Outcomes

Textio is more than a grammar checker; it is a predictive engine for candidate engagement. In 2026, its "Textio Score" is used by 25% of Fortune 500 companies to ensure their job posts appeal to a broad audience. By flagging subtle gendered phrasing like "rockstar" or "ninja," Textio helps companies attract a 40% more diverse applicant pool.

Beyond recruitment, Textio Lift helps managers provide fair and actionable performance feedback. This addresses the "retention" part of the DEI equation, ensuring that once diverse talent is hired, they are not pushed out by biased performance evaluations that often target underrepresented groups.

SeekOut: The Expert Hunter’s Tool

SeekOut is designed for specialized sourcing, particularly in highly competitive fields like AI engineering or aerospace. It goes beyond standard profiles by aggregating data from GitHub, Stack Overflow, and even patents to find talent that is invisible on LinkedIn. Its "Bias Reducer" mode allows sourcers to hide names and photos while searching, promoting a focus on technical qualifications from the very first step.

SeekOut’s "Coder Score" is a significant second-order insight for 2026; it rates developers based on the actual quality and frequency of their code contributions to the public domain, providing a data-backed alternative to traditional resume credentials.

HireVue: Standardizing Volume and Potential

HireVue is the choice for organizations that need to hire at a massive scale without sacrificing equity. By using asynchronous video interviews, HireVue ensures that every candidate is asked the same questions in the same format, which is a key predictor of job performance. Its AI-driven scoring assists recruiters in ranking thousands of applicants, though in 2026, most recruiters use this as a supportive signal rather than a final decision, maintaining the "Human-in-the-loop" principle.

HireVue’s psychometric games measure attributes like adaptability and creativity in a few minutes, providing a "potential" score that is especially valuable for early-career candidates who lack a long work history.

Pymetrics: The Neuroscience of Belonging

Pymetrics uses gamified assessments to measure 90+ cognitive and emotional traits. For example, the "Money Exchange" game evaluates a candidate's sense of fairness and decision-making, while the "Tower" game measures problem-solving speed and logic.Because these games are based on neuroscience rather than language or cultural experience, they are highly effective at reducing bias against candidates from different socio-economic or cultural backgrounds.

Pymetrics is praised for its ability to reduce turnover by matching candidates to roles where their natural traits align with those of the company's high performers. However, it is fundamentally a tool for evaluating future potential rather than hard skills, making it a powerful companion to technical tools like HackerEarth.

Direct Comparison of Technical Assessment Platforms

For organizations specifically focused on technical hiring, the landscape includes specialized competitors that offer varying degrees of DEI support.

Platform DEI Focus Talent pipeline
HackerEarth Anonymized assessments and global hackathons. Massive developer community; deep analytics; robust proctoring. No low-cost entry plans; can be overkill for small teams.
CodeSignal Realistic coding simulations. High-fidelity environment mimicking real work. Pricier; setup can be complex.
HackerRank Algorithm/puzzle-based tests. Industry standard; large library of 3,000+ challenges. Puzzle focus can feel abstract and irrelevant to real engineering work.
TestGorillae Mixed tech/non-tech roles. Broad skill evaluation; reduces bias across multiple role types. Coding depth is not as strong as specialized technical platforms.
Codility Automated screening. Efficient for large-scale standardized algorithm testing. Limited interactive interview support; test setup can be time-consuming.

HackerEarth’s unique advantage in 2026 is its "Candidate Experience" focus, offering multi-language support and practice tests that address the linguistic and institutional barriers often faced by underrepresented technical talent. While HackerRank and Codility focus heavily on algorithmic puzzles, HackerEarth’s hackathon-led approach allows for a more holistic evaluation of "Innovation Friction" and collaborative problem-solving.

Strategic Implementation of DEI Technology

Implementation of diversity recruiting software in 2026 must be viewed as a "behavioral recalibration" rather than a mere technical installation. Even the best software will fail if not supported by an inclusive organizational culture.

The 15-Step Diversity Audit Framework

  1. Organizations must start with a comprehensive audit of their existing funnel. This involves:
  2. Reviewing funnel data by demographic group to identify where minority candidates drop off.
  3. Analyzing the language in feedback to identify coded bias (e.g., “culture fit”).
  4. Ensuring diverse representation on interview panels to mitigate individual bias.
  5. Training panelists on bias spotting before every major hiring cycle.
  6. Setting clear, nuanced diversity metrics that go beyond general categories to include geography and socio-economics.
  7. Implementing blind resume reviews as a default setting.
  8. Mandating unconscious bias training for all hiring managers quarterly.
  9. Spotlighting real DEI commitments through authentic storytelling.
  10. Expanding outreach through partnerships with groups like NSBE or Out in Tech.
  11. Offering reasonable accommodations like captioning or flexible scheduling during interviews.
  12. Benchmarking progress against industry leaders like Salesforce or Google.
  13. Collecting and analyzing feedback from both hired and rejected candidates.
  14. Establishing protocol for “Algorithmic Bias Checks” with IT teams.
  15. Linking diversity goals to broader business resilience and risk man

Building Authentic Employer Branding

In 2026, "employer branding" has moved from social media marketing to "proof of culture." Candidates look for evidence that a company’s leadership is committed to DEI through action, not just words. Organizations should share the positive feedback from their diverse employees on their website and social channels, while also taking constructive criticism publicly to show a commitment to continuous improvement.3 Authenticity is key; tokenistic branding where a diverse group of employees is only shown during recruiting season will backfire and lead to higher attrition.

Economic Modeling and the ROI of Diversity Recruiting Software

The Return on Investment (ROI) for diversity hiring software in 2026 is calculated by comparing the gain from better hiring decisions against the total cost of ownership.

The Fundamental ROI Formula

The standard formula for recruitment ROI is:

To calculate "Total Recruitment Cost," organizations must include internal labor (recruiter hours

hourly rate), external spend (software licenses, job ads), and leadership time spent on panels.

Quality of Hire Formula

The "Quality of Hire" is a critical metric for 2026, as it reflects the long-term success of DEI efforts. It is calculated as:

High-quality hires contribute directly to the "Productivity Value" and "Revenue Impact" of the company, especially in roles where output is tied to creative problem-solving or sales.

Hard Cost Savings vs. Value Gains

Cost Factor Talent pool Talent pipeline
Cost Per Hire (CPH) Reduced reliance on external agencies through better internal "rediscovery". Filling one role internally can save $20k-$30k in agency fees.
Time-to-Hire (TTH) Automation of screening and scheduling via conversational AI. Filling roles 50% faster prevents lost productivity revenue.
Attrition Cost Lower turnover in inclusive environments. Replacing a bad hire costs 30% of their annual salary.
Innovation Friction Diverse teams solving problems faster and creating new products Top-quartile diverse companies are 36% more likely to be highly profitable.

In 2026, the ROI of DEI is also linked to "Psychological Safety." When teams feel safe to speak up, they identify risks faster and iterate more successfully. A "climate indicator" analysis often shows that after DEI-focused leadership recalibration, cross-team proposal submissions increase and issue escalation becomes faster, directly improving the "Innovation Rate".

Why Organizations Prioritize HackerEarth for DEI Initiatives

HackerEarth stands out in the 2026 market as the premier choice for organizations that view technical hiring as a critical DEI battlefield. Its platform is specifically engineered to handle the complexities of developer recruitment while maintaining a high standard of equity.

Industry-Leading Candidate Experience

HackerEarth’s commitment to candidate experience is a major differentiator. The platform allows candidates to take assessments in their native programming language and provides five regional language options for instructions. Features such as auto-complete, pre-populated snippets, and real-time error detection ensure that technical glitches do not unfairly disadvantage candidates. This focus on reducing syntax-related friction allows the candidate's core logic and ability to shine, which is essential for a "skills-first" 2026 hiring environment.

For more details on how these features enhance the hiring process, organizations can explore the candidate experience feature set.

Bridging the Global Talent Gap

By hosting virtual hackathons and coding challenges, HackerEarth enables companies to tap into a global community of 10 million developers. This massive reach is essential for organizations aiming for "Geographic Representation" and "Socio-economic Diversity". Unlike traditional university-hub recruiting, virtual events allow a developer in a remote region or from a low-income background to compete on equal footing with candidates from top-tier institutions.

For a deeper look into how these trends are shaping the future of early talent acquisition, teams can refer to the report on future trends in campus recruiting for 2025 and 2026.

Verified Skills and AI-Driven Validation

HackerEarth provides over 15,000 coding challenges across 1,000+ skills, including niche areas like emerging AI and data science. This "AI-driven skill validation" ensures that candidates are not just evaluated on their past roles, but on their ability to perform the specific tasks required for the job today. Detailed performance analytics and custom reporting allow hiring teams to track their funnel and identify where bias might be creeping into their technical evaluation process.

Synthesis and Concluding Recommendations

The recruitment landscape of 2026 confirms that diversity is no longer a "nice-to-have" but a fundamental driver of business excellence. The tools discussed—HackerEarth, Greenhouse, Textio, and others—provide the systemic infrastructure needed to move from good intentions to measurable equitable outcomes. However, the true advantage in 2026 goes to the teams that redesign their operating models around "Inclusion-by-Design," ensuring that technology serves as a human-enabling partner rather than a biased gatekeeper.

Organizations seeking to lead in 2026 should:

  • Adopt "Skills-First" and "Proof-First" hiring methodologies to prioritize capability over pedigree.
  • Implement rigorous "Algorithmic Governance" to ensure AI tools remain fair and explainable.
  • Focus on "Belonging" and "Psychological Safety" as key indicators of DEI success and retention.
  • Leverage "Pay Transparency" as a tool for building trust and attracting top-tier talent.
  • Utilize data-driven metrics to link diversity to innovation revenue and overall profitability.

In 2026, the question is no longer whether an organization can afford to hire diversely, but whether it can afford the economic and innovative risks of remaining homogenous. By choosing the right combination of tools—such as HackerEarth for technical validation and Greenhouse for process structuredness—organizations can build a resilient talent engine that is prepared for the complexities of the future global market.

Recruiting Time to Fill Metrics Explained

Time to fill: how to calculate this recruiting metric

In the increasingly complex theater of global talent acquisition, the ability of an organization to respond to vacancy pressures with both speed and precision has become a definitive marker of operational health. As the labor market of 2025 matures, characterized by a cooling US market but persistent talent scarcity in specialized sectors, human resources metrics have transitioned from simple tracking mechanisms to high-stakes strategic assets. Among these, the time to fill metric stands as a cornerstone for workforce planning, offering a comprehensive diagnostic of the entire recruitment lifecycle from the initial identification of a need to the successful acquisition of talent.

Improving this metric is far from a mere administrative exercise in acceleration; it represents a fundamental optimization of organizational resources. Every day a position remains unfilled represents a quantifiable loss in productivity, an increase in the burden placed upon existing staff, and a potential erosion of competitive advantage in fast-moving industries like technology and healthcare. By understanding the nuances of how to calculate, benchmark, and reduce time to fill, organizations can transform their recruitment function into a predictive engine that minimizes downtime and secures the highest caliber of human capital.

What is "time to fill"?

At its most fundamental level, time to fill is the time-based measurement of the gap between the recognition of a vacant role and the point at which the role is officially filled. It serves as a comprehensive indicator of recruitment efficiency, capturing the friction or fluidity of internal approval hierarchies, the effectiveness of external sourcing strategies, and the decisiveness of the selection process. Unlike other narrower metrics, time to fill provides a macro view of the organization’s ability to replenish its ranks and sustain project momentum.

Definition of time to fill

The formal definition of time to fill refers to the total number of calendar days required to identify, interview, and select a candidate for an open position. It essentially tracks the total duration of a vacancy within the organizational structure. While different companies may adopt slightly different start and end points depending on their internal workflows, the industry standard focuses on the period from job requisition approval to the candidate’s formal acceptance of an offer.

To visualize this process, one may consider the lifecycle of hiring a software engineer. The process initiates when a technical department identifies a capacity gap—perhaps due to a new product launch or a resignation—and submits a formal request to HR. The time to fill the clock begins ticking the moment this request is sanctioned by finance or executive leadership. The process then encompasses the drafting of specific technical requirements, the publication of the role on specialized job boards, the sourcing of passive candidates, and the execution of technical assessments. It continues through several rounds of interviews and the final negotiation stage. The measurement concludes only when the selected engineer formally signs the offer letter, signaling that the vacancy has been resolved.

Why time to fill matters

The importance of time to fill extends across several layers of business operations, from immediate financial impact to long-term strategic planning. For HR professionals, it is a primary tool for forecasting. If a company knows its average time to fill for a senior analyst role is 50 days, it can initiate the recruitment process nearly two months before a planned project expansion, thereby ensuring the new hire is ready to contribute exactly when needed.2

From an operational standpoint, this metric is a critical diagnostic of internal efficiency. A consistently high time to fill often suggests underlying dysfunction, such as misaligned expectations between recruiters and hiring managers, or an approval process that is overly bureaucratic. In the modern economic climate, where 50% of organizations struggle with losing talent to competitors during the hiring process, the ability to close roles quickly is directly linked to securing top-tier talent. Moreover, the financial burden of a vacancy often referred to as the cost of vacancy can be substantial, involving not only lost revenue but also the tangible costs of advertising and the hidden costs of team burnout.

How time to fill compares to other metrics

To fully understand the health of a recruitment pipeline, time to fill must be viewed in tandem with other key performance indicators. While it measures the total duration of a vacancy, related metrics like time to hire and cost per hire provide different analytical lenses.

Metric Primary Focus Measurement Interval Diagnostic Value
Time to Fill Organizational Efficiency Requisition approval to offer acceptance Evaluates the speed of the entire business process
Time to Hire Selection Agility Candidate application to offer acceptance Evaluates the candidate experience and recruiter speed
Cost per Hire Financial Investment Total recruitment spend divided by hires Evaluates the fiscal efficiency of talent acquisition
Quality of Hire Long-term Value Performance and retention data Evaluates the effectiveness of vetting and cultural fit

These metrics often interact in revealing ways. For instance, a short time to hire coupled with a long time to fill suggests that while the recruiters are moving fast once a candidate is found, there are significant delays in getting roles approved or sourcing initial interest. Conversely, if both metrics are elevated, it likely indicates a fundamental bottleneck in the interview or decision-making stages.

Why tracking time to fill is important

Tracking time to fill is a strategic imperative because it directly correlates with an organization’s bottom line and its reputation in the talent market. In the 2025 landscape, where job openings in many sectors still outpace the number of active seekers, the speed of the recruitment engine serves as a significant competitive differentiator. Organizations that fail to monitor and optimize this metric often find themselves trapped in a cycle of reactive hiring and operational instability.

The cost of unfilled positions

The financial implications of a vacancy go beyond the simple lack of a salary on the payroll. Every day a critical role remains empty, the organization experiences a loss in productivity that can manifest as delayed product launches, missed sales targets, or diminished client service quality. In specialized industries, such as technology or professional services, the absence of a single high-impact individual can stall an entire project team, leading to ripple effects across the department.

There are also significant "hidden" costs associated with unfilled roles. When a position is vacant, the workload is typically distributed among remaining team members. Over time, this leads to increased overtime expenses and, more critically, to employee burnout and disengagement. If left unaddressed, this strain can lead to further turnover, creating a self-perpetuating cycle where a high time to fill in one role leads to new vacancies elsewhere in the organization.

Impact on hiring decisions and speed

A robust understanding of time to fill enables data-driven decision-making regarding recruitment resources and strategies. When leadership can see that specific departments consistently exhibit a high time to fill, they can investigate whether those managers need more training, if the salary bands are uncompetitive, or if the interview process is unnecessarily cumbersome.

Speed is particularly critical in the current market because the most qualified candidates are often the most fleeting. Research into candidate behavior shows that application rates spike significantly when friction is removed; for example, application completion rates rise from 3.6% when the process takes over 15 minutes to 12.5% when it takes under five minutes. This implies that organizations with a slow, high-friction process are not only taking longer to fill roles but are likely failing to attract the most desirable, "low-friction" candidates in the first place.

Candidate experience and employer branding

The recruitment process is a candidate’s first in-depth interaction with an organization’s culture and operational style. A protracted time to fill, often marked by long periods of silence and multiple redundant interview stages, signals a lack of organization and a disregard for the candidate’s time. This negative impression can severely damage an organization’s employer brand, making it harder to attract future talent.

Furthermore, approximately 70% of job seekers report losing interest in a role if they do not hear back within a week of an interview. In a competitive environment, a slow time to fill is essentially a gift to competitors, who may move more decisively to secure the talent that your organization identified but failed to close. By optimizing this metric, HR teams demonstrate respect for the candidate's journey and position the company as an agile, talent-focused employer.

Time to fill vs Time to hire (and other related metrics)

Differentiating between time to fill and time to hire is essential for identifying where specifically a recruitment process is failing. While they are often conflated in casual conversation, their distinct starting points provide vastly different insights into the organizational versus candidate-facing aspects of recruitment.

What is time to hire?

Time to hire is a measure of the speed at which a candidate moves through the recruitment funnel once they have already applied or been identified as a prospect. It tracks the internal execution of the screening, interviewing, and offer stages for the final successful hire. This metric is highly indicative of recruitment agility and the effectiveness of the selection process.

Because it focuses solely on the candidate's journey, time to hire is typically shorter than time to fill. It ignores the pre-posting activities like budget approval and job description drafting, focusing instead on the efficiency of the "human" element of the search how fast the recruiter and hiring manager can evaluate talent and make a decision.

How time to fill and time to hire influence your hiring process

The relationship between these two metrics allows HR leaders to perform a "gap analysis" of their hiring operations. A high time to fill combined with a low time to hire suggests that the bottleneck is located at the very beginning of the process.This might be due to a slow internal approval chain or an ineffective initial sourcing strategy that fails to generate any applicants for several weeks.

On the other hand, if both time to fill and time to hire are high, it indicates that the delay is happening within the selection process itself. In this scenario, candidates are applying, but they are getting stuck in the "middle" of the funnel waiting for interview slots, undergoing excessive rounds of testing, or lingering in the final decision-making phase. Understanding this distinction allows HR to apply the correct "medicine" to the process, whether that means streamlining administrative approvals or automating interview scheduling.

Other key metrics in the hiring process

A comprehensive recruitment strategy integrates several metrics to ensure that speed does not come at the expense of quality or financial sustainability.

Metric Business Significance Talent pipeline
Offer Acceptance Rate Measures the competitiveness of the final offer and the candidate's desire to join Adjust compensation or improve employer value proposition if rates are below 80%
Source of Hire Identifies which channels yield the highest ROI and the fastest hires Reallocate budget toward high-performing channels like referrals or niche boards
New Hire Retention Indicates the accuracy of the vetting process and cultural fit Refine interview criteria if turnover is high in the first 90 days
Candidate Net Promoter Score Measures the health of the employer brand from the applicant's perspective Simplify the application process if scores are low

How to calculate time to fill (formula & method)

Calculating time to fill requires a consistent and disciplined approach to data collection. To ensure that benchmarks are meaningful, an organization must apply the same measurement criteria across all departments and roles.

Formula for single position

The standard formula for calculating the time to fill for an individual role is a simple subtraction of the start date from the end date.

It is important to use calendar days rather than business days for this calculation because the vacancy impacts the business every day, including weekends. If a role is approved on January 1st and the candidate accepts the offer on February 14th, the time to fill is 44 days. This provides a realistic view of the total duration the organization was without that specific capacity.

Practical Example: The Software Engineer Lifecycle

  1. Jan 10: Hiring manager identifies the need and submits the requisition.
  2. Jan 15: Finance approves the budget. (The "Time to Fill" clock starts).
  3. Jan 20: The job is posted on LinkedIn and HackerEarth.
  4. Feb 05: The final candidate applies. (The "Time to Hire" clock starts).
  5. Feb 25: After three rounds of interviews and a technical assessment, the offer is extended.
  6. Feb 28: Candidate accepts the offer. (Both clocks stop).

In this example:

  • Time to Fill = Feb 28 - Jan 15 = 44 Days.
  • Time to Hire = Feb 28 - Feb 05 = 23 Days.

Formula for average time to fill

To assess the macro-efficiency of the recruitment team, HR leaders calculate the average time to fill for all roles within a specific timeframe (e.g., quarterly or annually).

Calculating the average across departments can reveal significant variations. For example, the average time to fill for engineering roles (often 50-60 days) is typically much higher than for customer service roles (30-35 days). Monitoring these averages over time allows HR to set realistic Service Level Agreements (SLAs) with hiring managers.

How to handle variations in calculation

While the "Approval to Acceptance" model is the industry standard, some organizations may adjust the start and end points based on specific business needs.

  • Internal Transfers: For internal promotions or lateral moves, companies often start the clock when the internal vacancy is announced. The process is usually faster because sourcing and background checks are streamlined, but it is still critical to track this to understand the "ripple effect" of vacancies created when employees move.
  • Evergreen Roles: For roles that are constantly open due to high turnover or constant growth (e.g., warehouse staff), measuring time to fill for each individual seat can be complex. Organizations often track the "time to fill each individual slot" or the "average vacancy rate" for the department instead.
  • Mass Hiring: In campaigns where 50 people are hired simultaneously, organizations typically use the "median time to fill" or calculate the duration from the start of the campaign until the last offer is accepted to avoid outlier skewing.

What good looks like – benchmarks and industry norms

Benchmarking allows an organization to contextualize its performance against its peers. A time to fill of 40 days might be excellent in the tech sector but slow for a retail environment. In 2025, several industry-specific and regional trends are influencing these benchmarks.

Time to fill benchmarks by role

The seniority and technical requirements of a role are the strongest predictors of time to fill. More specialized roles naturally have a smaller pool of qualified candidates and require more extensive vetting.

Role Type Typical Time to Fill (Days) Key 2025 Factors
Entry-Level / Frontline 20 – 35 High applicant volume; speed of initial screening is critical
Mid-Level Professional 35 – 60 Technical and cultural fit assessments; multi-stakeholder interviews
Senior / Specialized Tech 60 – 90+ Candidate scarcity; intensive technical case studies; high "ghosting" risk
Executive Leadership 90 – 120+ Multi-stage due diligence; board-level approvals

In technical roles, the timeline can be even longer. For instance, high-performing engineers are often off the market within 20 days, but the internal processes of larger corporations can push the time to fill for these roles past 60 days.

Time to fill benchmarks by industry

Industry dynamics, such as seasonal surges and regulatory licensing, create distinct "rhythms" for recruitment.

Industry Average Time to Fill (Days) 2025 Trends and Observations
Technology 35 – 60 Driven by developers and cloud specialists; niche stacks take longest
Healthcare 49+ Impacted by credentialing and licensing requirements
Retail 14 – 28 Volume-driven; speed of mobile application is a major factor
Manufacturing 18 – 45 Skilled trades like CNC operators trend toward the longer end
Professional Services 28 – 50 Heavy focus on soft skills and culture fit interviews

The "Hiring Benchmarks" report for 2025 indicates that while applicant volumes are rising (up about 50 applicants per role compared to 2024), the time to fill has dropped slightly to 63.5 days from 67.7 days. This suggests that organizations are becoming more efficient at processing larger pools of talent through technology.

Time to fill benchmarks by region

Geographical factors, including labor laws and local talent density, play a significant role in recruitment speed. For example, hiring in Germany is historically slower (nearly two months) due to the mandatory involvement of Worker’s Councils.

In North America and Western Europe, the shift toward hybrid and remote work has both compressed and expanded timelines. It has expanded the candidate pool (compressing sourcing time) but added complexity to "culture fit" evaluations (expanding interview time).In the Asia-Pacific (APAC) region, rapidly growing tech markets often exhibit shorter time to fill benchmarks as companies prioritize speed to capture market share, though this is often balanced by lower retention rates.

Common bottlenecks in time to fill (and how to identify them)

Identifying bottlenecks requires a forensic look at the recruitment funnel. A bottleneck is any stage where candidates consistently experience delays or where the recruitment process halts due to internal friction.

Sourcing delays and candidate pipeline issues

The most common bottleneck occurs at the very beginning of the process: sourcing. If a company relies purely on reactive job board postings, it may take weeks to attract a single qualified applicant for a niche role. This delay is often compounded by vague job descriptions that fail to communicate the employer value proposition.

To diagnose this, HR teams should measure the "time to first qualified candidate." If this takes longer than 10 days, it is a sign that the sourcing strategy is ineffective or that the role is poorly defined. Moving from reactive posting to proactive "pipelining"—building relationships with talent before a role opens—is the standard solution for reducing this delay.

Interview scheduling bottlenecks

Scheduling is often the "hidden" time-killer in recruitment. The manual coordination of multiple calendars (the recruiter, the candidate, and three different busy managers) can easily add 5-10 days to the process for every round of interviews. This "calendar ping-pong" is particularly frustrating for top candidates who are likely interviewing at multiple companies simultaneously.

Organizations can identify this bottleneck by tracking the time between "candidate shortlisted" and "interview completed." If this gap consistently exceeds 5 business days, it indicates a need for automated scheduling tools that allow candidates to pick slots directly from available calendars.

Decision-making delays

The final bottleneck often occurs at the very end of the process. Even after finding the perfect candidate, many organizations struggle with "decision-making paralysis." This can be due to a lack of a structured evaluation framework, where stakeholders cannot agree on a candidate, or due to complex approval hierarchies for the final offer package.

If the time from "final interview" to "offer extended" exceeds 3 days, the organization is at significant risk of losing the candidate to a more decisive competitor. Implementing structured interviews with clear scoring rubrics can help stakeholders reach a consensus more quickly and reduce this friction.

Strategies & best practices to reduce time to fill

Reducing time to fill requires a multi-pronged approach that addresses both internal processes and external engagement. The most successful organizations treat recruitment as a continuous, rather than a episodic, activity.

Automate your recruiting process

Automation is the single most effective tool for compressing the hiring cycle. By offloading administrative tasks to software, recruiters can focus on the "high-touch" elements of candidate engagement.

  • AI-Powered Sourcing: Tools that automatically scan LinkedIn and other databases to identify candidates who match role requirements can save recruiters hours of manual searching.
  • Resume Screening: AI can parse thousands of resumes instantly, ranking them against job criteria and highlighting top candidates for immediate review.
  • Automated Communication: Keeping candidates informed of their status through automated "next step" emails reduces drop-off rates and maintains engagement without manual effort.

Employee referral programs

Referrals are a powerful lever for reducing time to fill because they effectively "pre-vet" candidates for both skill and cultural fit. Referred candidates typically progress through the funnel faster than cold applicants because there is already a baseline of trust established.On average, organizations that leverage robust referral programs can reduce their time to fill for professional roles by 10 to 20 days.

Continuous candidate sourcing

High-performing organizations maintain a "warm" pipeline of potential talent for critical roles. This involves regular engagement with passive candidates through talent communities, professional networking, and social media.12 When a role opens, the recruiter can go to this pipeline first, potentially identifying the right candidate within 48 hours and effectively bypassing the entire sourcing stage.

Analyze and optimise your hiring funnel

Optimizing the hiring funnel requires constant monitoring of "pass-through rates" between stages. If a recruiter identifies that 90% of candidates are being rejected after the technical assessment, it suggests that the initial screening criteria are not aligned with the assessment goals.By constantly tweaking these "levers," HR teams can ensure that only the most relevant talent moves forward, reducing the total time spent interviewing unqualified candidates.

Use-cases: How recruitment technology and HR platforms help manage time to fill

The shift toward AI-driven recruitment platforms has provided HR teams with unprecedented capabilities to manage the velocity and quality of their hiring. These tools are no longer just for storage; they are active participants in the recruitment process.

AI-powered candidate screening

In industries like software development, where a single job posting can attract hundreds of international applicants, manual screening is a major bottleneck. AI screening agents can conduct the first "pass" of applications, analyzing resumes and even conducting preliminary chat-based interviews to verify technical skills. Platforms like HackerEarth can reduce the time spent on early-stage screening by up to 75% by identifying the top 20% of candidates automatically.

Integrated job posting and applicant tracking

Modern Applicant Tracking Systems (ATS) serve as a centralized hub for all recruitment activity. By integrating with job boards and internal systems, they allow for "one-click" posting and automated tracking of every candidate's progress.This visibility allows recruiters to see exactly where a candidate is stalling and intervene before they disengage.

Data-driven recruitment decisions

Technology provides the data necessary to justify strategic shifts to leadership. For example, if a company is consistently seeing a 90-day time to fill for senior engineers, the HR leader can use data from platforms like HackerEarth to show how a specific technical assessment tool could reduce that timeline to 45 days. This transitions HR from a cost center to a strategic partner that can quantify the ROI of its technology investments.

Implementation roadmap for your organisation

Improving the time to fill metric is a journey of continuous improvement. This roadmap provides a clear structure for HR teams to begin this transformation.

Step 1: Define clear measurement points

Consistency is the key to useful data. The organization must define exactly when the "clock starts" and when it "stops."

  • Action: Meet with finance and senior leadership to agree on these points. Most organizations choose "Requisition Approval" to "Offer Acceptance."
  • Benefit: This ensures that when you report a 40-day time to fill, every stakeholder understands exactly what that means, avoiding confusion during budget discussions.3

Step 2: Collect historical data

You cannot improve what you do not measure. HR teams should gather 12-18 months of historical data to establish an internal baseline.

  • Action: Use your ATS or spreadsheet to calculate the average time to fill by department, role, and hiring manager.
  • Benefit: This identifies "hidden" bottlenecks and provides a baseline to measure the impact of your future optimizations.

Step 3: Identify bottlenecks and implement solutions

Analyze the historical data to find the "choke points" in your funnel.

  • If Sourcing is the issue: Implement an employee referral program or invest in AI-powered sourcing tools.
  • If Interviewing is the issue: Train hiring managers on structured interviewing or implement automated scheduling.
  • If Decision-making is the issue: Simplify the approval chain for offer letters and set clear feedback deadlines for stakeholders.

Step 4: Continuously monitor and optimise

Recruitment is dynamic. Market conditions, company growth, and technological shifts will all impact your metrics.

  • Action: Set up a monthly dashboard to review time to fill and other key KPIs. Use "pulse surveys" to gather candidate feedback on the process speed.
  • Benefit: This allows the organization to remain agile, adjusting its strategies in real-time to maintain a competitive edge in the talent market.

Optimise Your Time to Fill with HackerEarth’s AI-Driven Recruitment Solutions

In the specialized field of technical recruitment, the stakes for time to fill are uniquely high. Engineering talent is both scarce and highly mobile, meaning that every day of delay increases the risk of losing top-tier candidates. HackerEarth provides an integrated, AI-driven platform that addresses these challenges directly, helping organizations build elite engineering teams with unprecedented speed and accuracy.

HackerEarth’s platform streamlines the technical hiring lifecycle through several key features:

  • AI Screening Agent: This "always-on" agent replaces slow, manual resume reviews by autonomously evaluating candidates against specific role requirements and delivering structured, bias-resistant insights instantly.
  • Advanced Technical Assessments: With a library of 40,000+ problems across 1,000+ skills, HackerEarth allows recruiters to launch role-based tests quickly. The AI-driven auto-evaluation ensures that technical depth and code quality are assessed fairly and instantly, reducing manual grading time by up to 75%.
  • AI Interviewer: By automating the end-to-end technical interview process, organizations can eliminate the primary source of scheduling drag and decision latency. The AI Interviewer conducts structured conversations, evaluates both technical competence and communication, and generates detailed reports for the hiring team.

By integrating HackerEarth into the recruitment workflow, organizations can compress their technical hiring cycle to under 10 days, ensuring they secure the talent they need to drive innovation without sacrificing accuracy or candidate experience. In the modern competitive landscape, this speed is not just an advantage; it is a fundamental requirement for success.

Top 7 Online Coding Interview Platforms in 2026

When you’re gearing up for a technical interview in recent times, practicing on the right platforms can help you make it or break the interview. 

Today’s job market moves fast, and hiring teams expect coding candidates to think clearly, write clean solutions, and perform under pressure. Online platforms have stepped up to meet that need. They give you real coding problems to solve, timed environments that mimic live interviews, and some even let you run mock interviews with real people. 

In fact, about 60% of companies now use online assessment platforms to evaluate technical skills during remote hiring, including live coding and automated grading, making online coding interview platforms a core part of developer recruitment. Whether you want to drill data structures, simulate live coding screens, or just practice coding interview questions again and again, there’s a tool designed for that. 

In this article, we break down the top 7 online coding interview platforms that will help you practice smarter, stay confident, and land the job you’re aiming for.

The 10 Best Online Coding Interview Tools: A Side-by-Side Comparison

This table provides a side-by-side comparison of the top online coding platforms for interview use, highlighting essential features, strengths, and use cases. 

It helps recruiters and hiring teams quickly evaluate each online coding platform's interview performance, so you can identify the right solution for your technical hiring needs.

Tool Name Best for (Use Case) Key Features Pros Cons G2 Rating
HackerEarth FaceCode Best overall online coding interview platform Live pair programming, extensive question library, AI-powered interview agent, smart browser proctoring, global hackathons, enterprise integrations, and reliable uptime End-to-end collaborative interviews; AI summaries; strong proctoring; vast question library; GDPR & ISO compliance Limited deep customization; no stripped-down budget plans 4.5
CoderPad Best for real-time collaborative coding Private interviewer notes, multi-language support, closed captioning, waiting room, ATS integrations Highly intuitive and smooth real-time collaboration; “no setup” candidate experience; robust multi-language support Basic UI; limited advanced editor/debug features; focused primarily on live interviews 4.4
Codility Best for enterprise technical assessments AI collaboration assessment, seamless collaboration tools, and a standardized process Live coding + pair programming + whiteboard; AI integration; accessible candidate experience SQL tasks can be awkward; some contexts need manual setup; fewer custom task options 4.6
HireHunch (HunchVue) Best for AI-assisted screening Multi-language support, interview playback, proctoring alerts, unified dashboard Robust playback and review; proctoring alerts; customizable templates Free plan time caps; learning curve for setups; potential additional costs 4.6
PlayCode Best lightweight browser coding tool Instant link interviews, multi-file projects, real-time collaboration, AI assistant No signup needed; AI help & bug detection; fast browser preview Free tier limits can frustrate; limited advanced IDE features N/A
Mercer Mettl Best for campus & large-scale hiring Seamless pair programming, role-based assessments, digital ideation, code analysis, hackathons Scales for campus/enterprise hiring; project-based evaluations; auto-graded simulators Higher pricing for smaller teams; some advanced tools require training; limited deep reports 4.4
iMocha Best for skill intelligence Skills library access, insights & benchmarking, AI-SkillsMatch, Tara AI interviews, secure proctoring AI-powered insights & scoring; secure assessments; adaptive AI interviews Learning curve; test setup can be unintuitive; advanced reporting can need extra configuration 4.4

How We Evaluated These Online Coding Interview Platforms

Most online coding interview tools promise faster hiring and a better signal. Very few prove it under real technical scrutiny. 

To separate marketing claims from measurable value, we evaluated each tool against the standards modern engineering teams actually require today.

  • Live coding collaboration quality: We assessed how well each platform supports real-time collaboration between the interviewer and the candidate. Then, we looked for smooth code sharing, low-latency performance, and features such as cursor visibility, code playback, and collaborative debugging that reflect real development workflows.
  • Multi-language support: Engineering teams rarely hire for just one language. Hence, it’s not practical for them to run separate platforms for Python, Java, JavaScript, Go, or Rust. If teams switch tools, it fragments workflows, increases cost, and creates inconsistent candidate experiences. Robust online coding interview platforms provide deep, reliable execution environments across both popular and niche languages.
  • IDE and environment realism: We also examined how closely each built-in IDE replicates production setups, including debugging tools, terminal access, dependency management, and configuration flexibility. The closer the environment is to real-world engineering conditions, the stronger the hiring signal.
  • Question bank depth: If technology evolves quickly, your interview questions must evolve even faster. At the same time, widely circulated questions lose their effectiveness when candidates memorize answers from forums or online interview-coding practice sites. High-performing platforms provide role-specific libraries across frontend, backend, data, DevOps, and system design.
  • Structured evaluation rubrics: Consistency reduces bias. Platforms that enforce standardized scoring frameworks make it easier for hiring teams to compare candidates objectively. Clear rubrics also improve interviewer alignment and support more defensible hiring decisions.
  • Anti-cheating and integrity safeguards: What use is a coding interview platform if it cannot detect cheating? Hence, we evaluated plagiarism detection, proctoring features, browser monitoring, copy-paste controls, and suspicious behavior tracking. 
  • Enterprise integrations: Modern hiring teams operate within complex recruitment ecosystems. This is why we reviewed ATS compatibility, single sign-on support, API flexibility, and workflow automation. 
  • Reporting and analytics: We examined score breakdowns, performance benchmarking, and interviewer insights. In-depth analytics support faster and more confident hiring decisions.
  • Candidate experience: Every interview reflects your employer brand. Interface clarity, platform stability, accessibility, and overall usability all shape how candidates perceive your company. A frictionless experience increases completion rates and leaves a lasting positive impression.
  • Pricing transparency: Finally, we reviewed pricing clarity, scalability across team sizes, and the risk of hidden costs. Transparent pricing supports predictable hiring budgets.

The 7 Best Online Coding Interview Tools: An In-Depth Comparison

After testing and comparing a wide range of platforms, we selected the 7 best online coding interview tools that combine real-world coding environments, seamless collaboration, and more, to help candidates and hiring teams succeed.

1. HackerEarth FaceCode: Best overall online coding interview platform

Conduct efficient live coding interviews with HackerEarth FaceCode
Make smarter, faster hiring decisions with FaceCode

As an all-in-one online coding assessment platform, HackerEarth allows hiring teams to assess candidates’ coding abilities, problem-solving skills, and communication in real time, moving beyond static tests or traditional resume filters. Its FaceCode tool provides a state-of-the-art collaborative code editor, HD video chat, interactive diagram boards for system design, and a built-in library of over 40,000 questions. Teams can conduct live panel interviews with up to five interviewers in one session to assess a candidate’s coding skills, problem-solving, and collaboration abilities.

The AI-powered interview agent automates structured interview conversations based on predefined rubrics, adapts to candidate responses, and generates unbiased scoring. FaceCode stores full interview recordings and transcripts for later review, and masking personally identifiable information ensures fair assessments.

FaceCode integrates with ATS platforms such as Greenhouse, Lever, Workday, and SAP. It is GDPR-compliant, ISO 27001-certified, and maintains 99.99% uptime, making it reliable for both small- and large-scale hiring.

HackerEarth also connects companies to a global developer community of over 10 million through hackathons and hiring challenges, allowing teams to discover and evaluate talent in interactive ways. Smart Browser Proctoring ensures integrity by monitoring activity, blocking unauthorized tools like ChatGPT, and tracking audio, tabs, and IP location during interviews.

Key features

  • Live pair programming: Conduct collaborative coding sessions with real-time editing and shared whiteboards
  • Extensive question library: Access 40,000+ questions across multiple tech domains
  • AI-powered interview agent: Automate structured interviews and adaptive scoring
  • Smart Browser proctoring: Monitor for cheating with tab-switch detection, audio tracking, and IP restrictions
  • Community engagement: Run hackathons and coding challenges to discover talent globally
  • Enterprise integrations: Connect with ATS platforms like Greenhouse, Lever, Workday, and SAP
  • Enterprise-ready: GDPR-compliant, ISO 27001-certified, and 99.99% uptime ensure reliability at scale

Who’s it best for

  • Tech companies and large enterprises that need to scale collaborative technical interviews, assess coding skills in real time, and maintain fair and consistent hiring processes

Pros

  • End-to-end support for collaborative interviews
  • AI-driven interview summaries improve decision-making
  • Strong anti-cheat and proctoring features for remote sessions
  • Access to a massive, role-specific question library
  • Enterprise-ready with GDPR and ISO compliance

Cons

  • Limited options for deep customization
  • No budget-friendly, stripped-down plans

Pricing

  • Growth Plan: $99/month (10 interview credits) 
  • Enterprise: Custom pricing 

2. CoderPad: Best for real-time collaborative coding

Hire better devs with CoderPad's live coding interview platform

Run stress-free technical interviews with real-time collaboration. CoderPad allows interviewers and candidates to write code together smoothly without lag.  Candidates can join with just a link, no setup required, which reduces stress and makes remote interviews seamless. 

The platform supports embedded audio and video calling with closed captioning to improve communication. Interviewers can customize their experience with a waiting room, code autocompletion, and preferred key bindings, such as Emacs or Vim. Additionally, it records coding sessions, creating transcripts and playback options for later review. It also integrates easily with ATS systems or scheduling tools and includes ready-to-use questions for quick interview preparation.

Key features

  • Private interviewer notes: Take notes in markdown, share with colleagues, or keep private to support unbiased evaluations
  • Multi-language support: Conduct interviews in C, C#, C++, Java, JavaScript, Kotlin, Python, Ruby, and Swift 5 without switching tools
  • Closed captioning: Reduce language barriers and improve accessibility for all candidates

Who’s it best for

  • Technical interviewers, engineering managers, and distributed teams that need to run real-time collaborative coding interviews and support multiple programming languages

Pros

  • Simple and focused interface for live coding sessions
  • Smooth real-time collaboration between interviewer and candidate
  • Supports multiple languages and real coding environments

Cons

Pricing

  • Custom pricing

3. Codility: Best for enterprise technical assessments

Offer seamless technical interviews in a collaborative environment
Assess a range of candidate skills with Codility Interview

Codility Interview provides a seamless environment that combines video chat, an IDE, pair programming, and whiteboard functionality. Interviewers can standardize workflows or allow free-flowing discussions, while candidates showcase their skills in an intuitive interface.

The platform accelerates hiring by streamlining technical and system design interviews without sacrificing quality. This allows candidates to enjoy an interactive experience with instant feedback, which improves engagement and creates a positive impression of your company.

Key features

  • AI collaboration assessment: Monitor how candidates work with generative AI tools using Cody, the AI assistant
  • Seamless collaboration tools: Use video chat, whiteboards, and pair programming to facilitate real-time interviewer discussions
  • Standardized evaluation process: Assess all candidates fairly using the same technical standards and rubrics

Who’s it best for

  • Technical recruiters, engineering managers, and enterprise teams that need to run standardized, high-fidelity technical assessments and evaluate engineers at scale

Pros

  • Combines live coding, pair programming, and whiteboards for complete evaluation
  • Supports AI collaboration skills assessment with Cody
  • Provides a highly intuitive and accessible candidate experience

Cons

Pricing

  • Starter: $1200/user
  • Scale: $6000 per 3 users
  • Custom: Contact for pricing

*All prices are listed annually.

4. HireHunch: Best for AI-assisted screening

Evaluate candidate’s technical aptitude with HunchVue
Conduct live coding in 35+ programming languages

Powered by HireHunch, HunchVue allows interviewers to focus entirely on candidate evaluation with an all-in-one dashboard.  The platform supports over 35 programming languages, making it versatile for assessing developers across different tech stacks.

HunchVue records interviews and provides playback, allowing hiring teams to review sessions at any time. Advanced proctoring alerts monitor for suspicious activity and maintain integrity throughout the interview process. These features together create a comprehensive solution for AI-assisted technical hiring.

Key features

  • Multi-language support: Conduct interviews in over 35 programming languages to assess candidates across diverse coding stacks
  • Interview playback: Replay coding and video sessions to review performance in detail
  • Proctoring alerts: Detect suspicious activity and maintain a fair and secure assessment environment

Who’s it best for

  • Tech companies, hiring managers, and startups that want AI-assisted screening, unified coding interview environments, secure proctoring, and flexible multi-language assessments

Pros

  • Provides interview playback for detailed review
  • Includes proctoring alerts for secure assessments
  • Offers customizable templates to match hiring needs

Cons

  • Free plan limits sessions to 40 minutes, which may be insufficient for larger teams
  • Customizable setup requires a learning curve to use all features effectively
  • High-volume usage can lead to additional costs beyond standard plans

Pricing

  • Pay As You Need Plan: Custom pricing
  • Full Service Plan: Custom pricing

5. PlayCode: Best lightweight browser coding tool

Code seamlessly from any device with PlayCode
Interview developers online easily with PlayCode

Designed for fast, lightweight coding assessments, PlayCode reduces friction for both candidates and interviewers. All you need to do is share a link, and candidates start coding in seconds. The platform supports multi-file projects, real-time collaboration with multiple cursors, instant compilation, and live preview.

The best part is that it is significantly more affordable than many alternatives, costing as little as $5/month while still offering advanced features such as an AI coding assistant, code playback, and interactive review. Candidates can run JavaScript, TypeScript, React, and Vue projects without installing software or creating accounts. It also works well for first-round technical screenings, frontend or UI/UX interviews, pair programming exercises, take-home reviews, and mock interviews.

Key features

  • Instant link-based interviews: Share a project link and let candidates start coding immediately with no signup
  • Multi-file projects: Organize code into folders and files for complex, real-world scenarios
  • Coding AutoComplete: Enhance your coding speed and efficiency with smart code autocomplete feature

Who’s it best for

  • Tech companies, bootcamps, and startups that need a fast, affordable, and lightweight coding tool

Pros

  • No signup required for candidates
  • Offers AI coding assistance, bug detection, and instant code evaluation
  • Runs entirely in the browser with fast compilation and live preview

Cons

Pricing

  • Free
  • PlayCode Pro: $21/month billed yearly (100 credits)

6. Mercer Mettl: Best for campus & large-scale hiring

Conduct pair programming interviews with Mercer | Mettl 
Automate the hiring process with Mercer | Mettl

Conduct large-scale technical hiring efficiently with Mercer Mettl’s online coding interview tools. The platform helps you evaluate candidates in real-time using auto-graded simulators, live coding, and automated assessments.  Interviewers can monitor candidates as they code live, use digital whiteboards and notepads for ideation, and leverage data from previous screening rounds to make informed decisions.

The platform supports a broad library of pre-built questions for multiple job roles, enabling a quick start to interviews. It also facilitates holistic candidate evaluation, including behavior, cognition, and technical skills. Tools such as code playback, project-based assessments, and interactive hackathons provide deep insights into candidates' capabilities while maintaining fairness and consistency.

Key features

  • Digital ideation tools: Use interactive whiteboards and notepads to assess problem-solving and design thinking
  • Comprehensive code analysis: Leverage code playback and screening data from multiple rounds for objective evaluations
  • Pre-built question library: Access ready-to-use questions for major technical roles to accelerate the hiring process

Who’s it best for

  • Tech companies, large enterprises, and universities that need to conduct campus drives, large-scale hiring, or role-based coding assessments

Pros

  • Supports real-time live coding, digital ideation, and role-based assessments
  • Provides auto-graded simulators and project-based evaluations
  • Offers hackathons and coding projects to discover top talent

Cons

Pricing

  • Custom pricing

7. iMocha: Best for skill intelligence

Hire virtually with iMocha’s Live Coding Interview platform
Get comprehensive and accurate evaluations of candidates in real-time

iMocha’s Live Coding Interview platform enables recruiters to evaluate candidates in a seamless, interactive environment. The platform supports over 50 programming languages and frameworks, provides integrated code editors and compilers, and facilitates smooth candidate interaction with built-in chat and whiteboarding.

The platform also integrates with its AI-SkillsMatch and Tara conversational AI interview tools. AI-SkillsMatch maps job requirements to skills, evaluates candidate fit, and provides validated match scores. Tara conducts human-like, adaptive interviews with automatic scoring, transcripts, and bias-free evaluation at scale.

Key features

  • Skills library access: Use 10,000+ skills, including technical, cognitive, soft, and functional assessments
  • Insights & benchmarking: Compare candidates’ performance, generate detailed reports, and benchmark against peers
  • AI-SkillsMatch: Match candidates to job requisitions with validated skills profiles and AI-driven scores

Who’s it best for

  • Tech companies, enterprises, and staffing teams that need AI-powered coding assessments and scalable, bias-free technical hiring

Pros

  • Generates AI-powered insights, scoring, and benchmarking
  • Provides secure, proctored assessments with anti-cheating measures
  • Includes adaptive AI interviews via Tara for consistent evaluation

Cons

Pricing

  • Available in AI-SkillsMatch and Tara Conversational AI Interview plans: Custom pricing

Choose the Right Online Interview Coding Tool to Elevate Your Hiring

Technical interviews have evolved, and today’s hiring teams need tools that go beyond simple coding tests to assess collaboration, problem-solving, and real-world coding skills. 

Among all platforms, HackerEarth FaceCode stands out as an all-in-one platform with live pair programming, AI-powered scoring, and a massive question library. All these features enable teams to conduct fair, scalable, and efficient coding interviews.

Take your hiring to the next level! Book a demo or try FaceCode today and experience seamless, high-fidelity technical assessments.

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