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

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

Sergey: Thanks for having me, Sachin!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Links:
Twitter:@sfedov
Website:sfedov.com

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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:

  1. 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.
  2. 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.
  3. "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.
  4. Resume probing to three levels of depth. Pick one claim; ask what, how, and why. Proxies collapse at the second level.
  5. 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.

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.

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:

  1. AI screening filters the initial applicant pool against job-relevant skills (often a timed coding assessment).
  2. AI interview conducts a deeper evaluation on the candidates who passed screening — code written live, verbal explanation, follow-up probing.
  3. Human technical interview handles senior calibration, system design, and judgment-heavy conversations.
  4. 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:

  1. Deterministic rubric scoring. Same answer, same score, every time. Ask to see the scoring methodology, not just the output.
  2. Automated code execution. The platform runs candidate code against test cases rather than scoring the text of the answer.
  3. Role-calibrated question libraries. Pre-built content for the roles you actually hire for, not a generic question bank you configure from scratch.
  4. 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.
  5. Conversational follow-ups. The system asks the next question based on what the candidate said, not from a fixed script.
  6. 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.
  7. 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:

  1. 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.
  2. 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.
  3. 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. ```

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