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What’s wrong with today’s tech job descriptions?

What’s wrong with today’s tech job descriptions?

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Arpit Mishra
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September 21, 2017
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7 min read
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“Love brunch? Have we got a job for you? Live for brunch, drink an Aperol Spritz®, look great, and collect a paycheck — it’s a hard job but, hey, someone’s got to do it.” This job description for Chief Brunch Officers sounds too good to be true, doesn’t it?

But it is true. In 2014, Campari launched a wonderful social media campaign for Aperol lovers to spread the happiness of the delicious Italian aperitif, which has been touted as the most fashionable drink of 2017. Sigh! Although such dream roles are few, we’d settle for good jobs that at least sound appealing.

Job descriptions are what your applicants see before all else. It can accomplish so much if done right.

And, this is especially true in case of tech jobs.

When you ask for team players, whatever do you mean?

Do you mean they shouldn’t ideally question authority? Heaven, forbid.

Or, “Works with minimal supervision” means what? That if anything goes awry, he or she gets the blame possibly? Or it could just mean what it says: your manager is too busy to keep after you and expects you do your job.

Point being made: Enough with the meaningless, ambiguous job descriptions already!

It is really up to you how you want potential hires to perceive your organization and responsibilities that go with the roles.

Like The Adler Group CEO, Lou Adler, says, “It seems obvious that if a company wants to hire people who are both competent and motivated to do the work required, they need to start by defining the work required. Yet somehow this basic concept is lost when a new job opens up. Instead of defining the job, managers focus on defining the person. The end result is not a job description at all, but a person description.”

Most JDs demand you be a team player, be innovative, take initiative, show leadership skills and a willingness to learn, perform in a fast-paced environment, etc. Which applicant is actually going to admit a lack of these skills which you can’t test until much later anyway? How are these relevant in your very first advertisement of an open position? According to a Monster survey, 57% of applicants broke into a run the minute they spotted phrases such as “ninja,” “penetrate the market,” “rockstar developer,” “hit the ground running,” and “self-starter” in the JD.

When will they stop with the ill-defined job requirements?

Courting candidates is quite the order of the day now. A time when big companies could command as they wished is no longer possible. Today, highly skilled workers are in the driver’s seat. They get to choose who they want to work for and negotiate a lot more than they did before. So, companies really can’t afford to mess up while recruiting.

After analyzing best-performing job listings for a 6-month period, Stackoverflow found that “the average apply rate for the high-performing group was 30.9%, and the average for the lower was 3.2%.” One of the main reasons for their high performance was a clear and comprehensive JD.

Seriously outdated job descriptions

You know what is really irksome? Employers using antiquated job descriptions (JDs) that should have been binned a long time ago… If you can remember your job description for your current role, then take a bow. Not many of us remember what it said; it was so lackluster and generic. Half the time, it bears no resemblance to what we are doing now.

Incomplete, vague job postings

What’s the point in advertising for abstract skills instead of telling them how they will grow or what they will own, learn, and improve? Tell them what skills are absolute must-haves. Don’t ask them if they are going to be committed. (Like you’ll believe them anyway.)

Answer these questions before keying in the JD.

  • What is in it for the candidate?
  • Why should a developer feel excited about the company/role?
  • Are you describing enough about what your product is trying to achieve?
  • How is your product impacting the globe? (Developers will find one more reason to join you if they feel their work in the company has a larger agenda.)

    Confusing Ruby with a stone that’s red and shiny

Techies get it that a job role is more than a job. They get it that a job encompasses all sorts of qualities that are conventionally deemed non-job specific. However, they’d appreciate it if the recruiter knew if just knowing Java, and not Python, could jinx their chances. Talking to talent acquisition personnel who are clueless about the job requirements can’t be a whole lot of fun.

Unrealistic expectations

Companies advertise for developers who must know a string of programming languages. The tendency is to stuff the JD with many programming languages but, in general, a programmer is likely adept at not more than two or three. And what happens with the “over-optimization” of JDs is that some programmers use the languages as keywords in their resume. And eventually, this comes to bite the hiring managers when they go out to source and find that most programmers know almost half the languages on the planet. Over-optimization takes the fun away from life! Haven’t you seen this video – I miss the mob?

Ridiculous, impossible requirements

What’s really strange is when firms demand experienced professionals for jobs that are fairly new in the market. For example, if you advertise for programmers with 7 years of experience in a language that was introduced only 5 years ago, who exactly do you expect to get?

Also, before creating a JD, a recruiter should know the demographics and the sizable pool of a skill/requirement in a particular region. This sets realistic expectations and the JD will have more clarity.

Unheard of job titles

The Monster survey also found that 64% of the respondents were unlikely to apply for a job if the job title was not easy to understand. (Here’s an interesting infographic about the dilemma of job descriptions.)

According to an Australian Employment Office poll, 48% of employees say the role they were hired for isn’t the job they’re doing. For people in IT-related fields, misleading job titles are nothing new. How horrible it is when you sign on to be a project manager of an “entire group” and all you end up doing is leading a team of two (including yourself)! (It happens.) If you want a Technical Lead for Windows/Cloud, then say that and list the major skills instead of saying Technical Lead and giving a bunch of vague tasks.

How can bad job descriptions harm you?

With badly defined roles that helped you hire “talent,” you can expect to see poor productivity, higher absenteeism and turnover, and unhappy employees later on. Also, a survey showed that 78% of IT job postings are guilty of using meaningless jargon.

Rather than looking for Ivy League degrees, focus on the skills you need and tell them how they can grow with the company. It is ok to talk about the culture and the company, but not at the cost of a concise, clear, and comprehensive summary of key responsibilities. Culture and swag may win you good people, but you do need top quality talent to get the numbers going.

Sometimes, even imaginative JDs can translate into something awful or funny (if you’ve got a sense of humor). Jeff Bertolucci gave a Craigslist Wanted Ad a funny twist: Wanted: Skilled app developer who “will be paid from the profits of the app/business with a percentage stake in the company.” Translation: Until then, enjoy living out of your car. The point being that no-nonsense and clearly defined descriptions are a safer bet.

In today’s candidate-driven market, it pays to be savvy about every aspect of hiring. This makes streamlining their tech recruitment strategies imperative for hiring managers, talent acquisition officers, and recruiters. It doesn’t matter whether it’s something as high up the list as using online automated evaluation tools or crafting an attractive, realistic job description. It’s got to be well-designed if you want to have your share of great programmers in such a competitive industry.

On a side note, just what is a rockstar developer, a digital prophet, or a data science ninja?

The effect of poorly written job descriptions on tech hiring

  1. Attracting the wrong candidates: Poorly crafted job descriptions can attract applicants who do not align with the actual requirements or expectations of the role, leading to an influx of unqualified candidates.
  2. Missing out on high-quality candidates: Top talent may be deterred by vague, unrealistic, or overly complex job descriptions. Clear and realistic descriptions are key to attracting skilled professionals.
  3. Inefficiency in the hiring process: When job descriptions are not clear or accurate, it leads to a longer hiring process as recruiters and hiring managers spend time sifting through unsuitable applications.
  4. Damage to employer brand: Ambiguous or misleading job descriptions can harm a company’s reputation, as candidates may share their negative experiences with others or on social media.
  5. Diversity issues: Overly specific or unnecessarily stringent requirements can unintentionally exclude a diverse range of candidates, reducing the inclusivity of the hiring process.
  6. Increased turnover: If the role does not match the expectations set in the job description, new hires are more likely to become dissatisfied and leave the position, leading to higher turnover.

Tips to make your tech job descriptions better

  1. Be specific and clear: Clearly define the role, responsibilities, and required skills. Avoid jargon and overly technical language that might be unclear to potential applicants.
  2. Realistic requirements only: List only essential qualifications and skills. Overstating requirements can deter good candidates who might assume they’re underqualified.
  3. Highlight growth and learning opportunities: Mention opportunities for professional development, as many candidates in tech value continuous learning and career growth.
  4. Include information about company culture: Share insights into the company culture, values, and work environment. This helps candidates assess their cultural fit.
  5. Be inclusive: Use inclusive language to encourage a diverse range of applicants. Avoid gender-coded words and be mindful of language that may unintentionally exclude certain groups.
  6. Provide a clear application process: Outline the steps involved in the application process. This transparency helps set expectations for candidates.
  7. Salary and benefits: If possible, include a salary range and a summary of benefits. This transparency can be a significant factor in attracting candidates.
  8. Keep it concise: Avoid lengthy descriptions. A concise, well-structured job description is more appealing and easier to comprehend.
  9. Use a friendly tone: A conversational and friendly tone can make the job description more engaging and approachable.
  10. Get feedback: Before publishing, get feedback on the job description from current employees in similar roles to ensure it accurately reflects the position and your company culture.

PS: For more such insights on tech recruitment, we invite you to join our LinkedIn group – “Yours Truly HR”

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Author
Arpit Mishra
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September 21, 2017
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7 min read
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Vibe Coding: Shaping the Future of Software

A New Era of CodeVibe 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,...

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...

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...

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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