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The Go-Getter's guide to diversity hiring in tech

The Go-Getter's guide to diversity hiring in tech

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Soumya Chittigala
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March 3, 2020
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3 min read
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Over 47% of millennials want to work at diverse companies but a lack of workforce diversity and unconscious bias are fast becoming a systemic problem in tech. Companies worldwide are looking to tackle the issue of diversity hiring in tech but very few have actually made strides in the right direction.

A diverse workforce can give you a competitive advantage and has also impacted productivity and profits in the past. This guide can help you run a more diverse and inclusive hiring process in tech which could reap huge benefits for your organization in the long run.

Why does diversity in tech still matter in 2020?

How to limit bias in your tech hiring process?

7 ways to increase diversity in tech

How to get started: D&I tech hiring examples

Diverse teams deliver better results

Why does diversity hiring in tech still matter?

Diversity has always been a top priority for recruitment and talent acquisition. In fact, it’s been doing the rounds for quite some time but very few organizations have actually built a diversity hiring process that eliminates bias – both conscious and unconscious. Though companies have hiring initiatives and focus on diversity and inclusion within their teams, very few have a formal D&I program.

There could be 2 reasons for this –

  • Diversity fatigue – Diversity can be complex and it requires consistent effort by organizations and when this process gets slow or non-existent, fatigue starts to set in. This could lead to paralysis and inaction.
  • Bias – Bias seems to be more prevalent in tech roles and the talent gap doesn’t help the cause either. With increasing pressure on closing open tech positions, recruiters can get sucked into the vacuum of forming (biased) opinions about a candidate and using that as a means for making decisions, rather than objectively analyzing a candidate’s ability to code.

Finding ways to fix these issues can help you run a better and more diverse hiring process.

Inclusive teams

How to limit bias in your tech hiring process?

While eliminating bias altogether from the tech recruiting process could be a long shot, there are a few things we can do to ensure we reduce it. Here are a few ideas that can help:

(Be) Objective – Collaborative – Decisive

  • Be objective in your tech hiring process: Objectively measuring a candidate’s ability to code can help you overcome bias. A tech assessment platform should be able to mask details like name, orientation, gender, race, pedigree, etc. where bias is most likely to creep in. Evaluating candidates this way ensures that they make they cut based on their skills alone and nothing else.
  • Allow collaboration via inclusive panels: Biases mostly arise at an individual level. Replacing one on one discussions with an inclusive panel makes sure that it’s no longer an individual opinion that stands out, but a collective one. Collaborative interviews leave little room for bias and are the logical step to follow after an anonymous skill-based assessment
  • Take decisive actions: Despite taking all the necessary precautions to prevent bias, recruiters can sometimes be left with the decision to make the last choice and times like these call for decisive actions. Since, so far, we’ve tried to make the process as bias-free as possible, the final decision should be based on the candidate’s skills and nothing else. This way you limit bias while not limiting talent in your hiring process.
Inclusive teams make better decisions

7 ways to increase diversity in tech

1) Say “No” to racial stereotyping, legacy politics and pedigree

Many colleges in the US admit “legacies”, or students with a family connection to the university, at dramatically higher rates than other applicants. It could be a no-brainer to reach out to candidates from the top 10 schools but put that on hold. Why? – Because these schools are not very accessible to low-income students.

In order to be impartial in this process, keep your mind open to hiring diverse candidates both from ivy leagues as well as coding boot camps or state schools. While shortlisting candidates, focus on their work history and background and test their skills using a bias-free assessment platform

2) Aim for an inclusive digital transformation

Digital transformation is great for your organization but is it hurting your diversity hiring goals? Digital disruption is impacting the technology sector the most and organizations are struggling to keep up with the digital gender divide.

With no proper mentorship and upskilling opportunities available (especially for women), this gap is getting difficult to bridge. Instead, aim for an inclusive digital transformation by creating a community of female role models in tech and stimulate learning through mentorship and hands-on tech training workshops.

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3) Do away with resumes

A resume at best tells you what someone has done in the past and not what they’re capable of doing in the future. If the candidate is fortunate, his or her resume is first read by a human being rather than an automated Applicant Tracking System(ATS). Also, a resume almost always includes details like gender and educational background which could create an unconscious bias among employers.

Also, resumes can never tell you if the candidate codes well or not which is another reason why you should probably do away with them. Rather, there are other ways that you can use to shortlist potential talent. Administering a personality test and testing their job competency using a skill-based developer assessment can be good alternatives.

4) Combat ageism in tech

Tech’s youth-driven culture and workforce could make some developers and engineers feel obsolete. The best way for developers to combat ageism is to never stop learning and when you’re hiring senior engineers, it’s always good to consider the whole story.

One way of doing this is to examine what the developer has done in the past two years that is tangible to a prospective employer rather than looking at their 20 plus years of experience. Also, evaluate them on values rather than on a culture-fit.

5) Hire for value fit instead of a culture fit

An assessment of culture fit should focus on how well the person’s values align with the organization’s, rather than how well their personal characteristics, such as gender, ethnicity, age, and sexual orientation, align with the current workforce. Recruiters who interview based only on culture could form biased opinions and pick candidates who think or act like them. Instead, hire for values candidates who share the company’s vision and goal.

6) Make your job descriptions gender-fluid

Being mindful of the vocabulary in your job description can make a big difference in tech. Avoiding gender-coded words like “rockstar” and “ninja” can help weed out an unconscious bias against women developers. Also, emphasizing your company’s commitment to diversity and inclusion in your job description can help create a more inclusive workforce.

7) Look beyond diversity

Diversity is just the first step. Recruiters should foster a sense of belonging among employees by bringing differences together through inclusion. Allowing for freedom of expression, celebrating differences and understanding that diversity hiring is not just merely hiring women developers helps create a truly inclusive workplace for all. Here is a guide on driving inclusive hiring in tech.

Gender diverse teams

How to get started: D&I tech hiring examples

Here are some initiatives by companies we absolutely admire for being flag bearers of diversity hiring in tech:

Slack : Since 2015, the company has proactively sought out candidates from outside traditional developer pipelines such as Stanford and MIT. It has also focused on recruiting tech talent from all women’s coding camps such as Hackbright and programs that focus on training black and Latino programmers such as Code2040.

Intel: Intel has made the largest-ever commitment to invest in technology companies led by women, underrepresented minorities (African Americans, Hispanics, and Native Americans), startups led by entrepreneurs with disabilities, US-based entrepreneurs from the LGBTQ community, and US military veterans. Through September 2019, the Intel Capital Diversity Initiative has invested $381 million in companies led by diverse teams.

Buffer: Buffer uses its blog as a medium to explore issues in tech which affect underrepresented groups. The company also regularly modifies its job descriptions to include language and images that support inclusive hiring. They also sponsor awesome initiatives such as POCIT‘s Beer and Boardgames event and #wocintech‘s awesome photos

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Author
Soumya Chittigala
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March 3, 2020
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3 min read
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Vibe Coding: Shaping the Future of Software

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing code.

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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