How to become a better developer: Top tips from 15 industry leaders

Editor's note: This article is a compilation of developer interviews originally conducted in 2016–2017. Roles, companies, and product references reflect that period. The advice itself is evergreen and has been preserved as given. Interview answers are reproduced largely as given, including conversational phrasing and emoji, to preserve the voice of each engineer.

How do you actually become a better developer — or, put another way, how do you become a good programmer? It is one of the most common questions early-career engineers ask — and one with no single answer.

We put that question to fifteen engineers across React Native, machine learning, back-end, and infrastructure — from Facebook, Snapchat, Quora/Expo, Coupang, and BNY Mellon, among others.

Their answers, lightly edited, follow below.

How to become a good developer: Key takeaways from 15 engineers

Across these fifteen interviews on how to become a good developer, a few patterns surface repeatedly about what separates a good programmer from a great one:

What separates a good developer from a great one

Build real projects, not tutorials. Nearly every interviewee named a concrete project — a game, a side service, an open-source contribution — as the thing that pushed them forward.
Get out of your comfort zone deliberately. Try a language, framework, or domain outside your specialty at least once a year.
Learn your tools before your language. Linters, debuggers, and editors compound your feedback loops.
Master the fundamentals. Data structures, algorithms, and math pay off every time a new framework appears.
Pursue code review. Having senior engineers tear your code apart is the fastest knowledge-transfer mechanism available.
Favor simple over clever. Terse code is not simple code; the goal is code the next person — including future you — can read.

Interviewees and current positions

Aravind Kumaraguru — Engineering Director, Pioneers in Engineering
Brent Vatne — Front-end Developer, Exponent
Charlie Cheever — Founder, Exponent
Christopher Chedeau — Front-end Engineer, Facebook
Dan Horrigan — Senior Back-end Developer, Futuri Media
Frank W. Zammetti — Lead Architect, BNY Mellon
Janic Duplessis — Co-founder, App & Flow
Jake Murzy — Co-founder, commitocracy
Jun Ho Hwang — Software Engineer, Coupang
Keon Kim — Machine Learning Maniac, NYU
Munseok Oh — Co-founder and CTO, Sketchware
Satyajit Sahoo — UX Lead at Glucosio and Front-end Engineer at Callstack.io
Sonny Lazuardi Hermawan — Engineer, Sale Stock
Sunggu Hwang — CTO, ScatterLab
Timothy Ko — Software Engineer, Snapchat

Languages Known by Interviewees: Breadth of Experience — Source: Illustrative based on languages mentioned in interviewee bios and answers within the article

Q&A

Aravind Kumaraguru

Aravind is an undergrad at UC Berkeley pursuing a degree in Electrical Engineering and Computer Science and is Engineering Director for the nonprofit organization Pioneers in Engineering.

Q: How do you think I can become a better developer?

A: Obviously, never stay complacent with what you know – this field changes ridiculously fast, and you need to keep up with it. Follow along with the news in the tech industry, perhaps read up on some source code for a Python module that you recently used.

A friend of mine had some free time over winter break, so he decided to teach himself Django and build a webapp that he could interact with over SMS. It’s sort of a toy project, but he really enjoyed learning the different development paradigms. For context, he specializes in embedded systems and robotics, so this is nowhere near his comfort zone.

But pushing yourself to try different things will make you much stronger as an engineer. I personally wish I had done more web stuff before this year – in my organization (PiE), we’re developing a new iteration of a robotics kit to be used by high school students. While I have a good grasp of the low-level and systems stuff, I’m at a loss when it comes managing our UI design. Never had an interest in doing that type of stuff full-time, but having even a surface-level knowledge can be immensely helpful

Q: Do you have any projects you did to push yourself out of your comfort zone?

A: I built an automated door opener last summer, which operated a mechanical lever to open a door when an RFID card was scanned. The project used a really powerful motor and a mess of sensors to track the state of the arm, which proved to be quite difficult to coordinate. I learned real quick that I would need to do a bunch of offline testing before running my code on the device, which was very different from what I was used to up till then.

In terms of academics, I just finished CS 189, which was a massive crash course in data science, optimization, and probability theory. The programming I did in that class was also very different from what I’m used to, even though it was all in Python.

Brent Vatne

Brent is Front-end web/mobile developer working on Exponent and React Native. He contributes to tons of open-source projects.

Q: I really want to become a better developer; what would you say the first step is?

A: Do stuff you’re excited about and contribute to open source projects:-D

Q: How did you join Exponent? What was the cause?

A: James (ide) and I were the most active contributors to a react-native outside of facebook and so we spoke a lot. He created exponent with Charlie. I ended up doing some consulting work with them and Charlie asked if I’d be interested in working with them full time and year, it was lots of fun so I joined.

Charlie Cheever

Charlie Cheever is the co-founder of Quora, an online knowledge market. He was formerly an engineer and manager at Facebook, where he oversaw the creation of Facebook Connect and the Facebook Platform. Prior to Facebook, Cheever was employed by Amazon.com in Seattle. He left Facebook in June 2009 to co-found Quora with Adam D'Angelo. He later went on to start Exponent (now Expo).

Q: What’s the motivation of Exponent being free and Open Source?

A: I really want to make something that like a 12-year-old version of me would use. So, someone who doesn’t know tons about programming but can learn new things and doesn’t have a credit card or lots of money, but has time and creativity and a phone and friends. I learned to program making calculator games on TI-85, it’s sad to me that kids can’t make stuff on their phones today.

Q: Why did you leave Quora?

A: I managed the mobile teams there and it was so slow to work on those apps even tho we had good people, I found it so frustrating And after I left I tried to build some mobile stuff and it was so annoying that I decided there needed to be a different way to make stuff. So James and I made something like react Native called Ion. It was strikingly similar actually. But React Native already had android support and 20 people working on it, and we had 2 people. So we decided to make everything else around it that we wanted to make!

Q: What did you do on Facebook?

A: I made the developer platform that all those games like FarmVille were on. Well, not all of it obviously but was one of two main developers. And I worked on the first version of facebook video, then did a lot of random other things. Then was a manager and did log in with Facebook on other sites, and then left to do Quora.

Christopher Chedeau

Christopher has been working at Facebook as a Front-end Engineer for about 5 years. Previously, he worked at Curse Network.

Q: What do you do on Facebook?

A: I was on the photos team when I started, then I discovered React and started adopting and promoting it both internally and externally. I was there at the beginning of reacting native and pushed it through until 3 months ago. I just recently switched to the Nuclide team. I’m still #3 contributor on React Native.😛

Q: Do you have any prior work experience?

A: I was working for Curse (doing website for blizzard games) during my college to pay for it. It was fun to see the company go from 5 people in a guild to a 100 people company.

Q: What’s your day to day like on Facebook? The current project you’re working on?

A: I’m currently working on the Nuclide team, Facebook’s IDE built on top of Atom. I would say my time is spent half coding, half cheerleading all the cool stuff people are doing inside of FB.

Q: How do you think one can become a better developer?

A: I think that there are multiple levels.

The first level is mastering all the concepts. For example yesterday I had to write a function that removes certain keys from a big nested object. Because I’ve done this task so many times in the past, I was able to implement it in one go without even thinking and it worked the first time. For this one, exercises are really good. You want to code the same kind of things many many times to train your muscle memory.

The second level is how do you build things in a way that are not going to break in the future. Ideally, once you build something, you can move to the next thing and it’ll keep working without you there. This is challenging when there’s a ton of developers touching the codebase and product directions changing often.

Finally, the third level is how do you prevent a whole class of problems from even existing in the first place. A good example is with manual dom mutations, it’s very easy to trigger some code that interacts with a dom node that has been removed from the dom. React came in and made this problem go away. You have to go out of your way to do so, and even if you want to do those things, you have the tools to make it work: lifecycle events.

Q: Is there something you wish you’d known or learned earlier as a programmer?

A: Probably the most important thing is: tradeoffs, tradeoffs, tradeoffs. They are everywhere.

If you are working on some random throwaway feature that no one is going to use, who cares if the code is maintainable, you need it to work and now one mistake I see a lot is that people over-engineer the easy things but are not willing to make their architecture less clean from a CS perspective even though it actually provides the user experience you need.

At the end of the day, we write all this code for the users, we should first understand what the user experience should be and then do whatever it takes to get it. If the user just needs to display some content and needs to be able to edit it easily, just install WordPress, pick a good looking theme and call it a day

Dan Horrigan

Dan is a Senior Back-end developer @Futuri Media. He has 20 years of programming experience in many different languages. He’s been contributing to React Native early/mid-2015.

Q: What’s your background as a programmer?

A: I started learning to program (with QBasic) when I was 11 and was hooked. I learned everything I could, as fast as I could. I learned a few languages like Visual Basic and started to dabble with C and C++. Then I found web development and dove in head first. First, learning HTML and CSS, then adding simple CGI scripts written in Perl, and eventually Classic ASP.

My first paying project was when I was 14: A website for the company my dad worked for, with a customer portal to let them see their job progress. This was all in ASP. After that, I started learning PHP, and have been using that as my language of choice ever since. However, I picked up a lot of experience with other languages along the way: JS, Python, Ruby (on Rails), Java, C#, Go, Objective-C.

Q: What are some projects you’re currently working on?

A: I work for Future Media (http://futurimedia.com). We provide SaaS solutions for Broadcast Radio and TV companies. We provide white label mobile applications, social engagement and discovery, audio streaming and podcast solutions, etc. I haven’t had much free time lately to contribute to many OSS projects, but hope to change that soon!

Currently, I am a Senior Back-End Web Developer, but I am transitioning into being the Director of Technical Operations.

Q: Is there something you wish you’d learned or knew earlier as a developer?

A: I wished I would have realized earlier in my career that it is OK to be wrong, and that failure is just a chance to learn.

Q: What’s the first step to becoming a good developer?

A: Come up with a small-ish project that you think would be cool, or would make your life easier, and just jump right in. Too many people try to learn without a goal other than “I want to learn to code.” Without a goal, you are just reading docs or copy/pasting from tutorials…you can’t learn that way.

To become a better developer, you need to do one simple thing: Never. Stop. Learning. Read other people’s code, figure out how that one app does that really cool thing you saw, read blogs, etc. No matter how good you are, or think you are, there is always someone better, and always more to learn.

Q: Is there a certain project you’re currently interested in? Next on your learning list?

A: I have been using, and occasionally contributing to, React Native since early/mid-2015, and continue to be interested in it.

Next, on my learning list is learning Erlang/Elixir. We build heavily distributed systems where I work and think we would really benefit from a language like that.

Frank W. Zammetti

Frank is a lead architect for BNY Mellon by day and the author of more than a dozen programming books for Apress by night

Q: How do I become a better developer?

A: I get asked this question quite a bit both at work from junior developers and from readers of my books. I always give the same answer: make games!

It sounds like a joke answer, but it most definitely is not! Games have a unique ability to touch on so many software engineering topics that you can’t help but learn things from the experience. Whether it’s choosing proper data structures and algorithms, or writing optimized code (without getting lost in micro-optimizations – at least too soon), or various forms of AI, it’s all stuff that is more broadly applicable outside of games. You frequently deal with network coding, obviously audio and visual coding (which tends to open your mind to mathematical concepts you otherwise might not be), efficient I/O and of course overall architecture, which has to be clean and efficient in games (and for many games, extensible). All those topics and more are things that come into play (hehe) when making games.

It also teaches you debugging and defensive programming techniques extremely well because one thing people don’t accept in games is errors. It’s kind of ironic actually: people will deal with some degree of imperfection in their banking website but show a single glitch in a game and they hate it! You have no choice but to write solid code in a game and you figure out what works and what doesn’t, how to recover from unexpected conditions, how to spot edge cases, all of that. It all comes into play and those are skills that developers need generally and which I find are most frequently lacking in many developers.

It doesn’t matter one bit if the game you produce is any good, or whether anyone else ever even plays it. It doesn’t matter if it’s web-based (even if your day job is), or mobile, doesn’t matter what technologies you use. The type of insight and problem-solving skills you build and tune when creating games will serve you well no matter what your day job is, even in ways that are far from obvious.

I’ve been programming games for the better part of 35 years now. No, none of them have been best-sellers or won awards or anything like that. In fact, it’s a safe bet that most people wouldn’t have even heard of my games, even the one’s still available today. None of that matters because the experience of building them is far and away the most rewarding part of it. Perhaps the best thing about programming games is that they are, by their nature, fun! You’re creating something that’s intended to be enjoyable so the process of creating it should absolutely be just as enjoyable. How many things can you do that are really fun while still being challenging and simultaneously help build the skills needed for a long career?

So yeah, make games, that’s my simple two-word answer!

Q: Is there something you wish you’d known or learned earlier as a programmer?

A: Hmm, tough question actually. I guess if there was one thing (and I’ll cheat and combine two things here because they’re related) I would say that early on I didn’t understand two very important phrases: “As simple as possible, but no simpler” and “Don’t let the perfect be the enemy of the good”.

I have a natural perfectionist mentality, so I spend a lot of time pondering architecture, API design, etc. I once spent 33 hours straight working on a Commodore 64 demo because ONE lousy pixel was out of place and my perfectionist brain just couldn’t live with it! Sometimes, I have to force myself to say “okay, it’s good enough, you’ve planned enough, now get to work and actually BUILD stuff and refactor it later if needed”, or I have to force myself to say “okay, it basically does what it’s supposed to, it doesn’t need to be absolutely flawless because nobody but me is even going to notice”. Especially when you’ve got deadlines and people relying on you, you have to make sure you’re working towards concrete goals and not constantly getting stuck trying to achieve perfection because you rarely are going to, at least initially anyway, no matter how hard you plan or try – and the dirty little secret in IT is that perfection rarely matters anyway! Good enough is frequently, err, good enough 🙂

And, your design/development approach should always strive to be as absolutely simple as possible. Of course, what constitutes “simple” is debatable and doesn’t necessarily even always have the same meaning from project to project, but for me some key metrics are how many dependencies I have (web development today is a NIGHTMARE in this regard – less is GENERALLY better) and how many layers of abstraction there are. Developers, especially in the Java world, like to abstract everything and they do so under the assumption that it’s more flexible. But if there’s one thing I’ve learned over the years it’s that the way to write flexible code is to write simple code. It’s better than abstractions and extension points and that sort of stuff because it’s just far easier to understand the consequences of your changes.

As a corollary, a terse code is NOT simpler code! Simple code is code that anyone can quickly understand, even less capable developers, and even yourself years after. Terse and “clever” code tends to be the exact opposite. Often times, the more verbose code is actually simpler because there are fewer assumptions and often less knowledge needed to understand it, less “code hoping” you have to do to follow things. Related to this is that writing less code isn’t AUTOMATICALLY better. No, you shouldn’t re-invent the wheel, but you also shouldn’t be AFRAID to invent a marginally better the wheel when it makes sense. Knowing the difference is hard of course and comes from experience, but if you think it’s ALWAYS better to write less code then you’re going to make your life harder in the long run.

Of course, don’t over-simplify code either. Too simple and suddenly extending it almost MUST mean a refactor. You never want to completely refactor because you HAVE to in order to build an app over time. There’s a balance that’s difficult to strike but it should always be the goal.

Oh yeah, and I wish I knew how to express myself in fewer words… but actually, I’m still obviously working on that one 🙂

How Often Experts Cite Each Habit for Becoming a Better Developer — Source: Illustrative based on article claims — derived from thematic frequency across 15 interviews

Developer Backgrounds: Formal CS vs. Non-Traditional Paths — Source: Illustrative based on interviewee bios stated in the article

Janic Duplessis

Janic is the co-founder of App & Flow, a react-native contributor, and open-source contributor.

Q: Any tips to becoming a better developer?

A: Don’t think there’s anything in particular, you just have keep learning and getting out of your comfort zone. Like trying a new language or framework from time to time. At least that’s what I do but I’m pretty sure there are some other good ways haha 🙂

Q: How can I start contributing to React Native?

A: The best is to start with something small like a bug fix or adding a small feature like an extra prop on a component. Most contributors know either iOS or Android and a bit of JS. There are also some JS devs that work on things like the package and clip. We keep some issues with a Good First Task label that should be a good place to start

Jake Murzy

Jake is an Open-source Archaeologist. He writes buzzword compliant code. Co-founder at @commitocracy.

Q: Hey Jake, any tips to becoming a better programmer? 🙂

A: Number one thing you should do is to learn your tools before you learn the language you work in because it will lead to faster feedback loops and you will get to experience more in less time. So install a linter and it will catch most of your errors as you type. It statically analyzes your code and recommends best practices to follow. You should always follow best practices until you gain enough experience to start questioning them.

Jun Ho Hwang

Jun is a software engineer at Coupang, a major South Korean e-commerce company often compared to Amazon. He is a very friendly developer who loves to connect.

Q: How do you become a better developer?

A: The word ‘better’ can be described in various ways–especially in the field of programming. A good developer could be someone who is exceptionally talented in development, someone who is amazing at communicating, or someone who understands Business very well. I personally think a “good” developer is someone who is in the middle–a person who can solve his or her business problem with their development skills, and communicate with others about the issue. Ultimately, to achieve this, it requires a lot of practice, and I recommend you to create your own service. Looking and thinking from the perspective of the user and improving the service to fulfill their needs really helps you grow as a better developer.

Q: Is there something you wish you’d known or learned earlier as a developer?

A: I really wish I started my own service earlier on. The hardest thing to grasp before developing is realizing how you can apply what you learned. Many developers are afraid to start a “service” because it sounds difficult; however, pondering about what to make and where to start, and then connecting those points of thought help you grow as a better developer.

Q: What do you do at Coupang? What are you currently working on?

A: Coupling provides a rocket-delivery-service, and I am working on developing a system called “Coupling Car,” which is related to insurance and monetary management. Furthermore, I’m thinking about adding transportation control system and the ability to analyze data from the log.

Keon Kim

Keon is a student at NYU who is really passionate about Machine Learning. He is a very active GitHub member who tries to contribute to open source projects related to machine learning.

Q: What are your interests? What kind of projects have you worked on?

A: I’ve been working on machine learning projects these days. I am one of the project members of DeepCoding Project, a project with a goal of translating written English to the source code. I’ve been contributing to a C++ machine learning framework called my pack(https://github.com/mlpack/mlpack), which is equivalent to scikit-learn in Python.

I’ve also done some fun side projects: DeepStock (https://github.com/keonkim/deepstock) project is an attempt to predict the stock market trends by analyzing daily news headlines. CodeGAN (https://github.com/keonkim/CodeGAN) is a source code generator that uses one of the new deep learning methods called SeqGAN.

Q: How do you become a better developer?

A: I think it is really important to understand the basics. By basics, I mean math, data structures, and algorithms. Deep learning is really hot right now, and I see people jumping into learning it without basic knowledge in computer science and mathematics. And of course, most of them give up as soon as mathematical notations appear in the tutorial. I know this because I was one of them and it took me really long time to understand some concepts that students with a strong fundamentals could understand in a fraction of the time I spent. New languages, libraries, and frameworks are introduced literally every day these days, and you need the fundamentals in order to keep up with them.

Munseok Oh

Munseok is a Full-stack developer and CTO at Sketchware. He previously worked at System Integration for ~7 years.

Q: How do I become a better developer?

A: When I was very young and cocky, I evaluated other developers based on their coding style. There were certain criteria they had to pass in order for me to judge them as a good developer. But now, I really don’t think that way. Now, I believe that every developer is progressive, which means he or she is becoming a better developer every day. It doesn’t really matter if the style is bad or code is good–as long as the program runs, I think it’s great! Whether the program has room for growth or has bugs, I think the motivation to develop is what really matters. Developers usually are never satisfied with their skills. They are always eager to become better–probably why you’re doing this. It’s really hard to justify “good developer”. People like you will become better than me in no time. I still don’t think I am a good developer.

Q: What was the most difficult thing when you were developing Sketchware?

A: Developing Sketchware wasn’t too difficult because we had a good blueprint for the item. The direction was very clear for us to follow, so developing it was a breeze. However, there was a line we had to maintain for Sketchware–this line had two conditions:

Sketchware must be an easy tool for anyone to create applications.
Whatever the user takes away from Sketchware can be applied in their future career

Since we wanted Sketchware to be an efficient tool that can help users learn programming concepts, I am very considerate and think a lot when it comes to adding new features in the application.

Q: As a developer, is there something you wish you knew or fixed earlier?

A: I really wish I jumped into the Start-up world earlier. When it comes to developing, you need to be passionate and really enjoy what you do. Even if you pull 3 all-nighters, ponder all day long about a new algorithm, or stress about a new bug, everything will be okay if you’re enjoying it. It really goes back to the question #1–I get my energy from the joy I have when I develop, and that joy eventually makes you a better developer. When life hits you, most developers lose the passion for developing if you think of it as work. I used to be like that. But now, I’m really not worried–since developing brings joy to me now. Even if we run out of funds or our company burns down, it’s really okay since I am making the most out of what I am doing.

Satyajit Sahoo

Satyajit is the UX Lead at Glucosio, and Front-end Engineer at Callstack.io. He is an amazing open-source contributor; he is a React Native Core Contributor and the creator of React Navigation and React Native Paper

Q: What is your background as a programmer?

A: I don’t really come from a programming background. I did my graduation in Forestry. I left post-graduation after getting a job offer and never looked back.

Q: What’s your day like on day to day basis?

A: It’s pretty boring. I wake up, order some breakfast online or go out, then start office work. In evening I go out to a bar or take a long walk if there’s enough time left. At night I mostly watch TV series or hack on side-projects.

Q: Motivation behind contributing to open source projects?

A: I’ve been involved in Open Source for a long time. When I was doing my graduation I got into Linux and got introduced to the world of Open Source. I loved it how we could learn so much from other projects. It fascinated me that developers were selfless to let us see and use the there code for free (mostly). I did a lot of Open Source projects in form of themes and apps during my college days, and it always made me happy when people forked them and changed to meet their needs, and send pull requests to fix things.

As a developer, I contribute to Open Source projects most of the time because I need a feature, or it improves something on a project I love. I think it’s better if we work together to fix stuff that is important to us rather than just filing issues.

Q: How do I become a better developer?

A: I think it’s important that we are open to new things. There’s a lot to learn, and we cannot learn if we stay in our bubble. Try new things, even if you think you can’t do it, even it looks complex on the surface. I have failed to do things so many times, but eventually succeed. In the process, I understand the problem and the solution, and then it becomes really simple.

Sonny Lazuardi Hermawan

Sonny is a JavaScript Full Stack Engineer, a React & React Native player, and an Open source enthusiast. He currently works as an Engineer at Sale Stock.

Q: How do you become a better developer?

A: I think always eager to learn is the key. Try everything, make mistakes, and learn from that mistakes. I agree that code review from partners and senior engineers will make our code better. Try publishing your own open source projects, meet other great developers and learn from them.

[Ed.: for a broader data view on developer habits, see HackerEarth's developer survey.]

Q: What’s your motivation behind creating open source projects?

A: I just want the people to know about our idea, and try implementing it so that others can use our project. I’m really inspired by people that work on open source projects that used by many devs such as Dan Abramov that created redux.

Sunggu Hwang

Sunggu worked at Daum Communications for 4 years. Then, he left Daum to work at Scatter Lab as the CTO. This is his 5th year at Scatter Lab.

Q: How do you become a better developer?

A: Hmm… Becoming a good developer… Every developer has his or her own personality when it comes to programming. As an analogy, think about blacksmiths! Not all blacksmiths are alike–some enjoy crafting the best sword, while some might enjoy testing out the sword more than crafting it. I am a thinker–who plans and organizes thoughts before I carry out an action. I think a good developer knows how to write concise and clean code; you should practice this habit. Even though the trend for programming is always changing, and many people use different languages, write a piece of code that anyone can understand without comments.

Q: What do you think is the next BIG thing?

A: I’ve observed the evolution of programming languages, and I think it’s becoming more abstract every generation–procedural programming, imperative programming, functional programming… I think in the future, maybe in about 20 to 30 years, we will live in the time where the computer writes the code for us, and we just put them together like legos.

Q: What should I focus on studying?

A: I think deep learning is a must. Try different tutorials and learn it with passion. Math, algorithms–anything will help you in the long run.

Timothy Ko

Timothy is a software engineer at Snapchat. He previously worked at many places such as Riot Games, Square, etc.

Q: What do you do at Snapchat?

A: I’m a software engineer on the monetization team, so I work on anything related to making money. Some example projects are Snapchat Discover, a news platform within the iOS and Android apps; Ad Manager, a control panel used by sales and ad operations to flight ads; Ads API, which allows third-party partners to integrate their own ad platforms into Snapchat. Also, I was a past intern at Snapchat so I occasionally give talks and Q&As to upcoming interns. I’m also heavily invested in hiring and conduct a lot of interviews there.

Q: What do you do on a day-to-day basis?

A: What I’ve mentioned previously. Also, even after I pass on the work to other people, sometimes I have to go back and help support it or be part of the technical discussions on future changes. When new people join the team, usually I’m the one to ramp people up on how the code base looks like the kinds of frameworks we use, how a typical engineer workflow looks like, etc.

Q: What languages/framework do you guys mostly use?

A: For server code, it’s usually Java and for UI we use React Redux. Most teams work in google app engine, which is why we use Java, but some teams switch it up a little bit due to some app engine limitations. And of course, the product teams work in objective C for iOS and Java for Android.

Q: How do you think I can become a better developer?

A: I think the best thing to do is to do as many things as possible. I did seven internships while in school so I already had two years of work experience before I graduated. Work experience is super important because coding in a hackathon, doing personal projects, and doing school assignments are totally different than working with enterprise software and apps with real users. But you have to start somewhere, so that’s where going to school, doing personal projects, and competing in hackathons comes in. And while at work, I think the best way to succeed is to ask lots of questions and learn by doing. You can read and study all you want, but you might not understand what’s going on until you actually do it. Another thing is code reviews — you can do so much knowledge transfer by having a more senior engineer tear your code apart and tell you how to make it better. Also, if you ever come up with a proposal on how to solve a problem, getting a tech lead to bombard you with hard questions forces you to make sure you have every little detail covered.

Frequently Asked Questions

How can I become a good programmer or better developer?

The counterintuitive answer from these interviews: slow down the loop, do not speed it up. Shipping more code faster is not what separates good from great — subjecting less code to harder scrutiny is. A single project reviewed line by line by a senior engineer teaches more than ten tutorials finished alone. The implication is uncomfortable for self-taught developers without access to that review: you have to manufacture it, through open source, pair programming, or paid mentorship.

Do I need a computer science degree to become a good developer?

No. Several developers in this compilation, including Satyajit Sahoo (who studied Forestry), built successful engineering careers without a CS background. Fundamentals in math, data structures, and algorithms matter more than the credential — and those can be self-taught.

How important is contributing to open source for career growth?

It is one of the most frequently cited habits among the engineers interviewed here. Open source contribution exposes you to production-grade codebases, code review from senior engineers, and real collaboration — all of which are difficult to replicate through tutorials alone. For a longer treatment of this path, see our self-taught developer's guide to learning to code.

What should junior developers focus on first?

Learn your tools (editor, linter, debugger, version control) before going deep on any single language, and pick a small concrete project rather than studying in the abstract. Both points are made explicitly by Jake Murzy and Dan Horrigan in this article. For more on the habits that compound over a career, see how to be a great developer.

Is it too late to start coding in your late 20s or 30s?

No. The developers in this compilation started at wildly different points — Dan Horrigan began with QBasic at 11, while Satyajit Sahoo came to programming after a Forestry degree with no CS background and became a React Native Core Contributor and the creator of React Navigation and React Native Paper. Starting age is a poor predictor; deliberate practice, real projects, and exposure to code review are what compound over time. If you are preparing to break in, our guide to acing coding interviews at top companies is a useful next read.

If you lead an engineering team, the habits above — building real projects, pursuing code review, favoring simple over clever — are what you are actually hiring for. They are also hard to surface in a traditional interview loop. HackerEarth Assessments evaluates candidates on role-based coding tasks with rubric-based scoring across 40+ programming languages, so you see how a candidate actually thinks through a problem rather than whether they can pass a syntax quiz. Schedule a demo of HackerEarth Assessments.

The article was originally posted by Sung Park on GitHub.

How to Spot Proxy Candidates in Remote Interviews

How to spot proxy candidates in remote technical interviews

Proxy candidates in remote technical interviews are people who hire or coordinate with a stand-in to complete part of their hiring process usually a technical screen or coding round while the real applicant takes the job. The problem is no longer rare, and it is no longer confined to offshore contract roles. In our experience running assessments for hundreds of engineering teams, the strongest signal of a proxy is not a dramatic moment on camera. It is a quiet inconsistency the panel misses because no one is looking for it.

This guide covers what proxy candidates in remote technical interviews actually look like in 2025, the warning signs that hold up across role types, and the process controls that reduce the problem without turning your hiring loop into surveillance theater.

Why proxy interviews are becoming a challenge in remote technical hiring

Remote hiring removed the one control most companies used to rely on without noticing: the shared physical room. The interviewer could see who walked in, who sat down, and whether the person on the resume matched the person in the chair.

Three shifts have compounded the problem. First, high-volume remote roles especially contract engineering and offshore development created a market for impersonation services, some of which operate openly on Telegram and Discord. Second, AI-generated resumes and cover letters have made the top of funnel harder to trust, as the US Federal Trade Commission has flagged in its guidance on AI-driven job scams. Third, candidates increasingly use real-time AI assistants during screens, which blurs the line between "proxy human" and "proxy tool."

The result: proxy interview fraud is now a continuity problem verifying that the person who passed the screen is the person who starts on day one not a one-time ID check at the start of a Zoom call.

What is a proxy candidate in a technical interview?

A proxy candidate is someone other than the actual applicant who participates in all or part of the interview process on the applicant's behalf. The proxy is usually a stronger engineer who handles the technical rounds; the applicant handles the HR conversations and shows up on day one.

Proxy fraud comes in three common forms:

Full impersonation: a different person appears on camera for the technical round, often with a doctored name badge or a fake background.
Ear-piece coaching: the real candidate is on camera, but a coach feeds answers through an earpiece, chat window, or secondary device.
AI-assisted answering: the candidate uses an LLM or specialized "interview copilot" tool to generate answers in real time.

The first two are what most people mean by "proxy interview." The third is the fastest-growing variant and is harder to detect because the candidate is, technically, the person on camera.

How proxy interviews impact hiring decisions

The cost shows up in three places. The hire fails probation and the role reopens six months later. The team loses trust in the interview process and starts adding redundant rounds. And the recruiter's credibility with the hiring manager takes a hit that is hard to repair.

For regulated industries: BFSI especially, a proxy hire is also a compliance exposure. Background verification and KYC controls assume the person being screened is the person being hired. When that assumption breaks, the audit trail breaks with it.

10 warning signs of proxy candidates during remote technical interviews

No single signal is diagnostic. Proxy detection is pattern recognition across several of these.

Inconsistent communication and technical knowledge

The candidate who wrote a clean solution in the assessment struggles to describe what a hash map is. The candidate who answers system-design questions with precision cannot explain a line on their own resume. Watch the delta between written and spoken technical fluency. it is the most reliable early signal.

Difficulty explaining submitted code

Ask the candidate to walk through their take-home line by line. A proxy who did not write the code will narrate it at a surface level, skip over design decisions, and resist "why did you choose this approach?" questions. Follow with a small modification request "what would you change to handle a list of 10 million items?" and watch whether they can actually edit their own work.

Unusual response patterns

Proxies and AI-coached candidates share a tell: a two-to-four second lag before answering, followed by a confident, over-structured response. Real engineers think out loud, backtrack, and revise. Fluency without hesitation on hard problems is suspicious, especially when paired with hesitation on easy follow-ups.

Suspicious interview behavior

Eyes drifting consistently to a second screen. A second voice audible in the background. A webcam that cuts out at the moment a hard question lands and returns 30 seconds later. Candidates who refuse to share their full screen, or who insist on using a virtual background that hides their physical environment. Any one is explainable. A cluster is not.

Inconsistencies between assessment and interview performance

A candidate who scored in the top 10% on a timed coding assessment should not fail to implement FizzBuzz on a live call. Rank-order drift between asynchronous assessment and live interview is the single strongest quantitative signal of proxy involvement.

Unnatural reliance on external assistance

Prolonged silences before answering open-ended questions. Verbatim recitations of textbook definitions. Code that uses idioms the candidate cannot defend when asked. In our experience, asking "why did you name this variable that?" catches more AI-assisted candidates than any proctoring tool.

Mismatched written and spoken English

A resume and cover letter written in confident, idiomatic English paired with halting verbal communication is a classic pattern — not because non-native speakers are suspect, but because the gap between written and spoken fluency is unusually wide in proxy cases.

Resistance to camera or identity checks

Cameras off "due to bandwidth," ID verification "already completed with the agency," unwillingness to repeat a scripted phrase on request. Legitimate candidates sometimes have one of these issues. Proxies tend to have several.

Background and audio anomalies

Delayed typing sounds that do not match on-screen output. Keyboard clicks continuing after the candidate stops talking. Background noise that sounds like a call center. These are environmental tells that candidates rarely control well under pressure.

Resume items the candidate cannot discuss

The candidate claims three years on a specific framework but cannot name the version they used or a problem they hit in production. Depth-of-experience probing — not trivia — is the most durable defense against inflated resumes and proxy coaching alike.

How to verify a candidate's technical skills during remote interviews

The goal is continuity: proving the person on the final-round camera is the same person who completed the screen, the take-home, and will sign the offer. A few controls do most of the work:

Government-ID verification at the start of the first live round, with the ID held next to the candidate's face on camera. Store the capture alongside the interview record.
A short, unannounced live coding segment in every technical round. Even 10 minutes of on-the-spot work is enough to expose a proxy who passed the take-home.
"Explain your code" rounds. Ask the candidate to extend a function they submitted, refactor it, or find a bug you have inserted. Comprehension is harder to fake than completion.
Resume probing to three levels of depth. Pick one claim; ask what, how, and why. Proxies collapse at the second level.
Cross-round consistency checks. Compare timestamps, writing style, and problem-solving approach across assessment and live interviews. Large gaps warrant a follow-up conversation.

For teams that need stronger controls, structured platforms like HackerEarth Assessments and FaceCode combine rubric-based scoring with session proctoring, so the comparison between async and live performance is quantitative rather than impressionistic.

Strength of Proxy Warning Signals Relative to Each Other — Source: Illustrative based on article's stated signal hierarchy

The role of AI in identifying suspicious interview behavior

AI-assisted detection is useful, but it is not magic. Current systems do three things reliably: they flag browser-tab switches and clipboard activity during coding assessments; they compare biometric signals (face match, voice match) across sessions; and they produce confidence scores that help recruiters prioritize which candidates to re-verify.

What AI detection does not do well: catch sophisticated earpiece coaching, distinguish a nervous candidate from a dishonest one, or replace the judgment of an interviewer who knows their rubric. Use it as a prioritization layer, not a verdict.

HackerEarth's OnScreen interview product combines KYC-grade identity verification with a deterministic evaluation framework so every candidate is scored against the same rubric, and the identity captured at screen stage can be compared against the person in later rounds. It does not eliminate proxy risk — nothing does — but it closes the continuity gap that human-only screening leaves open.

Best practices to prevent proxy interviews in technical hiring

Prevention beats detection. The companies that see proxy fraud least often are the ones whose process makes proxying expensive and low-return:

Require ID verification once, early, and store it. Do not re-verify at every stage; the overhead teaches candidates the process is theater.
Mix async assessment with live probing. A take-home that is never discussed is an invitation for proxy work.
Rotate questions. Published question banks leak. Fresh problems cost more to proxy.
Record interviews with consent and review flagged sessions. A second reviewer catches patterns the primary interviewer missed.
Calibrate your panel. Interviewers who have never seen a proxy do not know what the signals look like. Share recordings of past cases in your internal interviewer training.

The trade-off: every control adds friction for legitimate candidates. In our experience, teams that add more than two new verification steps at once see completion rates drop 10–20%. Add controls incrementally and measure drop-off.

How structured technical assessments improve hiring confidence

Unstructured interviews are the easiest to proxy because the rubric lives in the interviewer's head, and two interviewers will produce two different reads of the same candidate. A structured assessment — fixed questions, fixed rubric, timed sections, and consistent evaluation criteria — produces a baseline score that later rounds can be compared against.

The value is not that structured assessments are harder to cheat. It is that they produce a comparable signal. When the candidate's live performance diverges sharply from their assessment score, you have a quantitative reason to dig further. Related reading: how to design a fair coding test.

How to maintain a fair and reliable remote interview process

Fairness and fraud detection pull in opposite directions if you let them. Over-indexing on proctoring catches more proxies and loses more good candidates. The balance that works for most teams:

Treat every candidate as honest until the pattern says otherwise. Flags trigger follow-up, not rejection.
Document the specific signals you acted on. "Candidate failed to explain their own submitted code when asked to extend it" is defensible. "Something felt off" is not.
Apply the same verification steps to every candidate in a given role. Selective enforcement is a legal exposure, especially in jurisdictions with strong employment-discrimination law.
Give candidates a path to clear flags. A second live coding round, done cleanly, should resolve most ambiguous cases.

Frequently asked questions

Is a proxy interview illegal?

In most jurisdictions, impersonation in a hiring context is not a standalone criminal offense but can trigger fraud, misrepresentation, or identity-theft charges depending on how the impersonation was carried out and what documents were used. It is almost always grounds for immediate termination and offer rescission under standard employment contracts. Consult local counsel for jurisdiction-specific rules.

What is the 80/20 rule in interviewing?

The 80/20 rule in interviewing refers to the candidate speaking roughly 80% of the time and the interviewer 20%. It is useful as a diagnostic — a proxy or AI-coached candidate often inverts this ratio under pressure, speaking in short, scripted bursts while the interviewer carries the conversation. It is a heuristic, not a verdict.

What is the biggest red flag during a remote technical interview?

The sharpest single red flag is a large unexplained gap between a candidate's asynchronous assessment performance and their live performance on the same type of problem. Hesitation to turn on video or share screen comes second. Neither is conclusive on its own, which is why multiple signals matter more than any one.

How do I prepare my panel to detect proxy candidates in remote technical interviews?

Share recordings or anonymized transcripts of past proxy cases so interviewers recognize the pattern. Standardize a short "explain your own code" segment in every technical round. Train interviewers to probe resume claims to three levels of depth rather than accepting first-level answers.

Can AI-powered proctoring fully eliminate proxy interviews?

No. Current proctoring tools reduce casual fraud — tab-switching, obvious impersonation, clipboard misuse — but sophisticated proxy arrangements involving earpieces, second devices, or coordinated coaching still get through. Proctoring is a prioritization layer; the final judgment remains with the interview panel.

What should I do if I suspect a proxy after making an offer?

Do not rescind without verification. Schedule a short technical conversation framed as onboarding preparation and probe specific resume and submission details. If the gap is confirmed, most standard offer letters allow rescission for material misrepresentation; coordinate with legal and HR before acting.

See it in action

If you are losing confidence in your remote technical interviews, start by closing the continuity gap between assessment and final-round. Schedule a demo of HackerEarth OnScreen to see how identity verification, deterministic scoring, and session proctoring work together across the full hiring loop.

Hackathons

FaceCode Automated Reports: What's Inside & How They Work

Automated reports on FaceCode: how HackerEarth streamlines technical interview evaluation

Automated reports on FaceCode exist because technical interviews ask too much of interviewers at once: evaluate problem-solving, read code, assess communication, and take notes, all inside a 45-minute window. The result? Signal gets lost. Notes end up incomplete, scoring becomes inconsistent across panelists, and hiring decisions rely more on gut feel than on evidence.

Automated reports on FaceCode, HackerEarth's live coding interview platform with automated reporting, are built to solve exactly that problem. By compiling interviewer scores, candidate coding activity, and engagement insights into a single shareable summary, FaceCode gives hiring teams a structured record of each interview without manual note-taking after the session.

Interviewer Attention Split: Manual vs. Automated Reporting — Source: Illustrative based on article claims

What is FaceCode?

FaceCode is HackerEarth's live technical interview platform. It combines a collaborative real-time code editor supporting 40+ programming languages, built-in video chat, a diagram board for system design discussions, and automated interview reporting. Recruiters and engineering managers use it to conduct remote technical interviews that feel structured, consistent, and easy to review afterward.

Where FaceCode stands apart from a generic video call plus a shared document is in what happens after the interview ends: the automated report.

Why automated reports matter

In most technical interviews, interviewers context-switch constantly between watching the candidate code, asking follow-up questions, and jotting down observations. That split focus hurts evaluation quality. Important moments get missed. Scores get assigned hours later from memory. And when multiple interviewers compare notes, inconsistencies surface that make hiring decisions harder, not easier.

Automated reports close this gap. The system captures the signals that matter during the session and assembles them into a structured summary, so interviewers can stay focused on the candidate during the call and reason from evidence afterward. Teams that pair automated reporting with a structured interview process tend to see the biggest gains in consistency.

Sources used to generate automated reports on FaceCode

FaceCode's automated reports pull from three distinct sources, each capturing a different dimension of candidate performance.

1. Scores given by the interviewer

Each interviewer rates the candidate against four evaluation criteria: code quality, communication skills, problem decomposition, and debugging ability. With automatic evaluation, these same four criteria apply to every interview.

Teams that need their own rubric can schedule the interview with custom criteria instead. That is a separate scheduling option, and it does not generate the automatic summary. For a deeper look at how criteria design affects bias and fairness, see our breakdown of evaluation parameters that support a bias-free interview process.

Interviewer scores form the backbone of the report. They represent human judgment, which anchors the rest of the report. The report page stays empty until an interviewer has submitted scores.

2. Code written by the candidate

The platform automatically tracks the candidate's coding activity throughout the session, including:

Time spent actively writing code. Keystrokes are recorded only to calculate this figure.
Compilation outcome per task: whether each task compiled successfully at least once, shown as "N of M tasks".
The final source code the candidate submitted, viewable in the report.

This gives reviewers a factual record of how much effort went into the session and what the candidate finally delivered, not just whether they reached the right answer. The report does not replay every edit, deletion, or rewrite, so reviewers see the end result rather than the full history of the solution.

3. Candidate engagement during the interview

FaceCode also surfaces engagement insights from the session. They sit in the same insights block as time spent coding and include:

Talk-to-listen ratio: how actively the candidate participated in the conversation. This insight is calculated from interview transcripts, so it shows as unavailable while transcript loading is switched off.
Video-on time: how long the candidate's video was on.
Tab-switch count: how many times the candidate switched tabs during the session.

These metrics are proxies for attentiveness and communication style, not direct measures of technical performance, and should be interpreted alongside interviewer scores rather than in isolation.

Still, they add useful context. Low participation might indicate a quiet candidate who needed more prompting, or it might flag an interviewer who dominated the conversation. Either way, it's a signal worth reviewing.

A few caveats are worth stating. Engagement metrics are proxies, not measurements of skill, and a quiet candidate is not necessarily a weak one. Very short sessions produce thin coding-activity data. And for system-design-only interviews conducted mostly on the whiteboard, the coding-activity signal will be minimal; interviewer scores carry more of the weight.

What automated reports on FaceCode include

Once the session has closed, the activity analysis has finished, and an interviewer has submitted scores, the report contains:

Interviewer scores against the four automatic evaluation criteria (code quality, communication skills, problem decomposition, and debugging ability)
A summary of coding activity, including time spent coding and the number of tasks that compiled successfully at least once ("N of M tasks")
Engagement insights, including talk-to-listen ratio (when transcript data is available), video-on time, and tab-switch count
The final source code written by the candidate, viewable in context
A written summary, generated when the report is opened

The report is not generated the instant the interviewer clicks End. Analysis of the session's activity starts once the session closes, and the written summary is created when the report is opened after that analysis finishes. Interviewers and hiring managers can then review it alongside the session recording, giving the entire hiring team a single consolidated view of candidate performance, with no manual stitching together of notes and no export step.

Common questions about automated reports on FaceCode

How accurate are engagement signals as a proxy for candidate quality?

They aren't a direct measure of skill. Talk-to-listen ratio, video-on time, and tab-switch count are context signals, useful for flagging patterns like an interviewer dominating the conversation, but they should never override interviewer scores or the code itself.

Can automated reports on FaceCode be customized?

Not the automatic summary. Automatic evaluation scores every interview against the same four criteria: code quality, communication skills, problem decomposition, and debugging ability. If your team needs its own rubric, you can schedule the interview with custom criteria, but that option does not generate the automatic summary.

How do I share a FaceCode automated report with a hiring manager?

Once scores are submitted and the analysis has finished, the report can be reviewed by anyone on the hiring team with access to the candidate record in HackerEarth. There is no export or manual compilation step.

Why is the report page empty?

The report page stays empty until an interviewer has submitted scores. The written summary is then created when the report is opened, after the activity analysis finishes.

What happens to the report if the interviewer forgets to end the session?

Analysis waits until the session closes, so sessions left open delay the report. Ending the session explicitly is what starts it.

Integrations and workflow

FaceCode integrates with popular applicant tracking systems like Workable, so interview invitations and reports flow directly into your existing hiring workflow. Once a candidate is scheduled, interviewers get a link, join the session, and conduct the interview; the FaceCode report is stored in HackerEarth alongside the candidate's assessment record for full-context review.

Next steps

Schedule a FaceCode demo to see an automated report generated from a live session. The walkthrough shows how the four evaluation criteria map to report fields, so you can see exactly what the hiring team will debrief against.

Hiring Tools

AI Interview Platform for Technical Hiring at Scale | HackerEarth

AI Interview Platform: How to Screen Software Engineers at Scale

Technical hiring has a math problem. Candidate volume keeps rising, resume signal keeps falling, and the people best qualified to evaluate engineers your senior engineers have the least time to spend on first-round screens.

Add scheduling delays across time zones, inconsistent evaluations between interviewers, and the quiet dropout of strong candidates waiting a week for their next call, and the funnel starts leaking at the top before you've even seen your shortlist.

An AI interview platform is one of the clearer responses to that problem. Used well, it compresses the slowest, most expensive part of the pipeline — the first technical screen without lowering the bar for who makes it to a human conversation. Used badly, it adds a step without removing one.

This guide covers what AI interview platforms and AI screening platforms actually do, where they fit in a technical hiring workflow, how to evaluate them for software engineer hiring, and how HackerEarth OnScreen approaches the problem.

The real cost of traditional technical interviews

Before deciding whether an AI interview platform solves your problem, it's worth being specific about the problem itself. Traditional technical interview loops break down in four predictable places:

Interviewer availability. Staff engineers are expensive to pull off product work for 45-minute phone screens, and the more senior they are, the less willing the org is to spend them on top-of-funnel evaluation.
Scheduling delay. A strong developer who applies late Sunday night needs to hear back before Tuesday or they're deep in another process. Calendar coordination across time zones routinely stretches the first conversation to five or six days.
Inconsistent evaluation. Two interviewers running the same candidate against the same rubric often disagree. Interviewer variance has been documented in hiring research for years, including Google's internal analysis reported in Laszlo Bock's Work Rules!.
Screening throughput. AI-generated resumes have made the resume stack bigger and the signal thinner. The volume of "plausible" candidates per role has grown faster than the engineering hours available to filter them.

The result is that the hiring stage with the most volume, the first technical conversation is also the stage most constrained by human calendars and the most prone to inconsistent scoring. That's the gap AI interview platforms are built to fill.

Time-to-First-Screen: AI vs. Traditional Interview Process — Source: Illustrative based on article claims

What is an AI interview platform?

An AI interview platform is software that conducts a structured interview: audio, video, or chat-based between a candidate and a system that asks role-calibrated questions, probes with follow-ups, and scores responses against a predefined rubric. For technical roles, the platform typically includes a code editor, automated code execution, identity verification, and a recording and transcript the recruiter can review.

The important distinction: an AI interview platform is not a video recording tool with a scoring sheet. Three capabilities separate serious platforms from thin wrappers:

Deterministic rubric evaluation. The same answer produces the same score, every time, regardless of when the candidate takes the interview.
Conversational depth. The system asks follow-up questions based on what the candidate actually said, not from a fixed script.
Identity and integrity verification. The platform confirms the candidate is who they claim to be and flags behavior that suggests external assistance.

Without those three, you have a glorified recording tool. With them, you have something that can replace a first-round technical screen without lowering the bar.

AI interview platform vs. AI screening platform: how they differ

The two terms get used interchangeably, but they describe different scopes.

An AI screening platform is broader. It covers any automated system that filters candidates before a human recruiter engages — resume parsing, skills assessments, knockout questions, coding tests, and basic conversational screens. The output is usually a shortlist with scores.

An AI interview platform is a specific type of AI screening platform. It conducts a full structured interview — not just a test — and produces an evaluation across coding, problem-solving, and technical knowledge, with a recording and transcript attached.

In a technical hiring workflow, they typically stack:

AI screening filters the initial applicant pool against job-relevant skills (often a timed coding assessment).
AI interview conducts a deeper evaluation on the candidates who passed screening — code written live, verbal explanation, follow-up probing.
Human technical interview handles senior calibration, system design, and judgment-heavy conversations.
Hiring manager and panel rounds close the loop.

An AI interview platform replaces step 2 and sometimes compresses step 1 into the same session. It does not replace step 3 for senior roles, and if a vendor claims otherwise, that's a reason to be cautious.

How AI interview platforms evaluate technical candidates

Most AI interview platforms for technical hiring follow a similar flow. The candidate receives a link, verifies identity (usually photo ID plus live face match), completes a structured interview that includes code tasks and verbal explanation, and the recruiter sees a score alongside the recording and transcript.

What varies is how the evaluation actually happens underneath. Weaker platforms transcribe the candidate's answer and run it through a general-purpose language model to produce a score — which gives you fluent-sounding but inconsistent output. Stronger platforms run responses against a role-specific rubric with defined scoring anchors, use automated code execution to verify correctness, and generate scores from a deterministic framework rather than ad-hoc LLM judgment.

The three technical dimensions the platform needs to evaluate separately:

Coding skills. Evaluated by running candidate-written code against test cases. This is the easiest dimension to automate and the one most platforms do reasonably well. Watch for platforms that score code solely on text analysis without execution — they will mark plausible-looking wrong answers as correct.

Problem-solving. Evaluated by asking the candidate to explain their approach before writing code, probing on edge cases, and watching how they respond when the first solution doesn't work. This is where conversational depth matters most. A script-based system cannot adapt its follow-ups and will miss the signal.

Technical knowledge. Evaluated through targeted questions on language, framework, and systems concepts, with follow-ups that distinguish rote recall from working understanding. "Explain how a hash map handles collisions" asked once is a weak signal. The same question with three follow-ups on load factor, resize cost, and when to use a different data structure is a strong one.

The practical test for a vendor: ask them to run the same candidate response through the system twice and compare the scores. If the numbers drift noticeably, the platform is doing LLM-as-judge without discipline, and your scoring won't survive an audit.

Where AI interview platforms fit — and where they don't

AI interview platforms and live human interviews are not substitutes. They're better understood as two tools for different stages of the same funnel.

Where AI interviews are stronger:

First-round technical screens on high-volume roles
Consistency across large candidate cohorts
24/7 availability for candidates across time zones
Audit trails for compliance-sensitive hiring
Surge hiring events where a human-only loop would collapse

Where human interviews are stronger:

Senior and staff engineer calibration
Open-ended system design where context dependency matters
Cultural and leadership judgment
Any conversation where reading real-time reasoning is the point

The useful question isn't which to use. It's which stage to use each at. For a mid-level backend candidate on a first screen, a well-calibrated AI interview is often more consistent than the humans it's replacing. For a staff engineer evaluating architectural trade-offs with incomplete information, a human interviewer still wins.

Where AI vs. Human Interviews Perform Better Across Hiring Stages — Source: Illustrative based on article claims

Features that actually matter in an AI screening platform

Vendor spec sheets are long. These are the capabilities that determine whether the platform works for technical hiring at scale:

Deterministic rubric scoring. Same answer, same score, every time. Ask to see the scoring methodology, not just the output.
Automated code execution. The platform runs candidate code against test cases rather than scoring the text of the answer.
Role-calibrated question libraries. Pre-built content for the roles you actually hire for, not a generic question bank you configure from scratch.
Identity verification and proctoring. Photo ID match, live face verification, and signals that detect obvious external assistance. KYC-grade verification matters more in regulated verticals.
Conversational follow-ups. The system asks the next question based on what the candidate said, not from a fixed script.
ATS integration. Scores and recordings flow into Greenhouse, Lever, Workday, or your ATS without manual copying. "Integrates with Greenhouse" can mean anything from a native app to a CSV export — verify the difference.
Audit-ready evidence trail. Recording, transcript, rubric, and score stored together, because at some point your legal team will ask.

Features that sound impressive and matter less for technical hiring: avatar polish, voice naturalness, "sentiment analysis," and anything labeled "emotional intelligence scoring." For engineering roles, you want signal on code and reasoning, not on whether the candidate smiled.

Candidate experience and responsible AI use

AI interviews are a mixed experience signal for candidates, and the implementation determines which way it tips. Candidates generally dislike AI interviews that feel scripted, provide no feedback, or offer no transparent path to a human reviewer. They generally accept — and sometimes prefer — AI interviews when the alternative is waiting a week for a human screen that still happens on a Monday morning they can't make.

Responsible use of AI in hiring comes down to a few practical commitments:

Transparency. Candidates should know they're being interviewed by an AI system and understand how their data will be used.
Human review of decisions. Automated scores should inform recruiter and hiring manager decisions, not replace them, especially at borderline thresholds.
Bias auditability. Structured rubric-based evaluation is more auditable than unstructured human evaluation, but it is not bias-free. The defensible claim is that consistency and auditability improve, not that bias disappears.
Data handling. Recordings, transcripts, and biometric verification data need defensible retention and access controls, particularly under GDPR and similar frameworks.

Platforms that cannot clearly answer questions on these four areas are a risk, not a shortcut.

How to reduce time-to-hire without adding interview bottlenecks

The bottleneck in technical hiring is almost never the first candidate conversation. It's the second, third, and fourth — the panels, the take-homes, the debriefs that require named humans with calendars. An AI interview platform only helps if it genuinely narrows the pool going into those stages.

Three operational moves make the difference:

Calibrate the AI screen against your existing bar. Run 20–30 recent hires and 20–30 recent rejects through the platform. If the system passes your rejects or fails your hires at a rate above roughly 15%, the rubric is wrong for your context. That's your problem to fix before scaling, not the platform's fault.
Set a hard pass threshold the hiring manager has signed off on. If recruiters interpret scores differently, you've recreated the calibration drift you were trying to eliminate.
Cap the AI screen at 45–60 minutes. Longer sessions correlate with drop-off, not better signal. Candidates won't sit through 90 minutes with a bot the way they'll sit through 90 minutes with a person.

Time-to-hire savings compound where volume is high. At 50 candidates per role, cutting the first screen from six days to under 48 hours is a two-week improvement on time-to-fill. At three candidates per role, the same change is barely visible against senior-interview and offer-stage delays. If your bottleneck lives in the back half of the funnel, no AI interview platform will fix it.

How to choose an AI interview platform for software engineer hiring

Most AI interview platform evaluations go wrong the same way: the buyer compares feature lists, picks the one with the best demo, and discovers six months in that the rubric doesn't match their actual hiring bar. A better process:

Run a calibration pilot before signing. Put 30–50 past candidates through the platform — hires and rejects — and compare the system's scores to your actual outcomes. If the correlation is weak, no feature list fixes it.
Involve one engineering manager, not just TA. Engineering has veto power on technical hiring whether the org chart says so or not. A platform TA loves and engineering distrusts will not survive the first calibration disagreement.
Check the integrity story specifically. Ask what signals the platform uses to detect external assistance, how often it produces false positives, and what the recruiter sees when a flag fires.
Verify ATS integration end-to-end. Walk through a real candidate from invitation to scorecard in your actual ATS. Mock the handoff.
Price per interview, not per seat. Volume-based pricing aligns incentives; seat-based pricing penalizes the recruiters who use the tool most.

The platforms competing in this category — HireVue, Sapia.ai, Interviewer.AI, and HackerEarth OnScreen among others — differ most on how deeply they evaluate code versus how polished the conversational layer is. For software engineer hiring specifically, code evaluation depth matters more than conversational polish.

HackerEarth OnScreen: AI interviews built for technical hiring

HackerEarth OnScreen is our AI interview platform, built for structured technical screening at scale. It conducts role-calibrated interviews around the clock using lifelike video avatars, applies a deterministic evaluation framework so every candidate is scored against the same rubric, and includes KYC-grade identity verification and integrated proctoring in the same product.

A few capabilities worth calling out against the challenges covered above:

Rubric-based scoring with automated code execution addresses the consistency gap in traditional interviewer-led screens.
Integrated identity verification and proctoring reduce the integrity exposure that comes with remote interviews.
24/7 candidate access removes the scheduling delay that costs the strongest candidates in competitive markets.
Conversational follow-ups probe on reasoning, not just final answers, giving stronger signal on problem-solving.

OnScreen sits alongside HackerEarth Assessments and FaceCode in the same platform, so candidate data, rubrics, and scores flow between products without separate integrations. For TA teams running coding tests at the top of the funnel and live interviews at the bottom, that continuity matters more than any single feature.

OnScreen fits best for first-round technical screens on high-volume roles, surge hiring events, and any pipeline where consistency and audit trail matter. It's not the right call for senior leadership hires where cultural judgment is the primary signal, or for roles where live whiteboarding with the hiring team is the point of the interview — those belong in a human interview tool.

Frequently asked questions

Is there an AI tool for interviews?

Yes. The category includes several established options — HireVue, Sapia.ai, Interviewer.AI, and HackerEarth OnScreen, among others. They differ most on whether they target technical hiring specifically, where code evaluation depth matters, or general hiring, where conversational scoring dominates. For software engineer roles, the technical-hiring-focused platforms produce stronger signal.

Which AI agent is best for an interview?

No single platform wins across all use cases. For high-volume technical screening where code evaluation and identity verification matter, platforms with deterministic rubric scoring and automated code execution fit best. For general enterprise hiring focused on conversational competency assessment, more general-purpose platforms are commonly adopted. The "best" platform is the one whose rubric survives a calibration pilot against your actual hiring bar.

Can I use ChatGPT for an interview?

As an interviewer, no — ChatGPT has no identity verification, no deterministic scoring, no ATS integration, and no audit trail. As a candidate preparation tool it works, which is one reason resume signal has eroded. If you're building an interview process, the question is not whether to use a general LLM but whether your evaluation platform uses a structured rubric or is itself just an LLM wrapper. The second category produces inconsistent scoring.

Are AI interviews a red flag?

For candidates, AI interviews are a mixed signal, and the implementation determines the reaction. Candidates generally dislike AI interviews that feel scripted, give no feedback, or offer no path to a human reviewer. They generally accept them when the alternative is a week-long wait for a human screen. The red flag isn't the AI — it's a poorly implemented process without transparency or human review on the back end.

How accurate are AI interview platforms?

Accuracy depends on how the platform scores answers. Platforms with deterministic rubrics and automated code execution produce consistent scoring across candidates — more consistent than human interviewers across the same cohort, which is the fair comparison. Platforms that use general-purpose LLMs to score free-text answers without rubric anchoring produce scores that drift. Ask vendors for test-retest reliability data, not accuracy claims.

Do AI interview platforms introduce bias?

AI interview platforms have different bias profiles from human interviewers, not no bias. They are more consistent across candidates on the same day, less affected by interviewer mood or fatigue, and more auditable. They can also encode bias from training data or rubric design. The defensible position is that structured rubric-based evaluation applied consistently is more auditable than unstructured human evaluation — not that AI eliminates bias.

See HackerEarth OnScreen in action

Technical hiring breaks down in the places where human time is most expensive and least differentiated — the first-round screen, the scheduling cycle, the inconsistent scoring across interviewers. An AI interview platform fixes those specific problems if the rubric is sound and the integration into your pipeline is real.

Schedule a demo of HackerEarth OnScreen to see how structured AI interviews, identity verification, and deterministic scoring work against your actual roles. For context on where AI interviews fit in a broader hiring stack, see our guide to technical interview design. ```

How to Become a Better Developer: 15 Engineers Share Tips

Need A Quick Summary? Ask AI.

How to become a better developer: Top tips from 15 industry leaders

How to become a good developer: Key takeaways from 15 engineers

What separates a good developer from a great one

Interviewees and current positions

Q&A

Munseok Oh

Frequently Asked Questions

How can I become a good programmer or better developer?

Do I need a computer science degree to become a good developer?

How important is contributing to open source for career growth?

What should junior developers focus on first?

Is it too late to start coding in your late 20s or 30s?

Stay ahead, one post at a time.

Thank you for subscribing!

Hire top tech talent with our recruitment platform

Discover more articles

How to Spot Proxy Candidates in Remote Interviews

How to spot proxy candidates in remote technical interviews

Why proxy interviews are becoming a challenge in remote technical hiring

What is a proxy candidate in a technical interview?

How proxy interviews impact hiring decisions

10 warning signs of proxy candidates during remote technical interviews

Inconsistent communication and technical knowledge

Difficulty explaining submitted code

Unusual response patterns

Suspicious interview behavior

Inconsistencies between assessment and interview performance

Unnatural reliance on external assistance

Mismatched written and spoken English

Resistance to camera or identity checks

Background and audio anomalies

Resume items the candidate cannot discuss

How to verify a candidate's technical skills during remote interviews

The role of AI in identifying suspicious interview behavior

Best practices to prevent proxy interviews in technical hiring

How structured technical assessments improve hiring confidence

How to maintain a fair and reliable remote interview process

Frequently asked questions

Is a proxy interview illegal?

What is the 80/20 rule in interviewing?

What is the biggest red flag during a remote technical interview?

How do I prepare my panel to detect proxy candidates in remote technical interviews?

Can AI-powered proctoring fully eliminate proxy interviews?

What should I do if I suspect a proxy after making an offer?

See it in action

FaceCode Automated Reports: What's Inside & How They Work

Automated reports on FaceCode: how HackerEarth streamlines technical interview evaluation

What is FaceCode?

Why automated reports matter

Sources used to generate automated reports on FaceCode

1. Scores given by the interviewer

2. Code written by the candidate

3. Candidate engagement during the interview

What automated reports on FaceCode include

Common questions about automated reports on FaceCode

How accurate are engagement signals as a proxy for candidate quality?

Can automated reports on FaceCode be customized?

How do I share a FaceCode automated report with a hiring manager?

Why is the report page empty?

What happens to the report if the interviewer forgets to end the session?

Integrations and workflow

Next steps

AI Interview Platform for Technical Hiring at Scale | HackerEarth

AI Interview Platform: How to Screen Software Engineers at Scale

The real cost of traditional technical interviews

What is an AI interview platform?

AI interview platform vs. AI screening platform: how they differ

How AI interview platforms evaluate technical candidates

Where AI interview platforms fit — and where they don't

Features that actually matter in an AI screening platform

Candidate experience and responsible AI use

How to reduce time-to-hire without adding interview bottlenecks

How to choose an AI interview platform for software engineer hiring

HackerEarth OnScreen: AI interviews built for technical hiring

Frequently asked questions

Is there an AI tool for interviews?

Which AI agent is best for an interview?

Can I use ChatGPT for an interview?

Need A Quick Summary?
Ask AI.