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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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Remote Proctoring vs Smart Browser: How to Choose

Meta title: Remote Proctoring vs Smart Browser: How to Choose Meta description: Remote proctoring vs smart browser — what each catches, what each misses, and how to pick the right integrity layer for technical assessments today.

Primary persona: Recruiter / Head of Talent Acquisition running technical hiring at scale.

Remote proctoring vs smart browser: what each catches, what each misses, and how to choose

Remote proctoring and smart browser tools solve overlapping but distinct integrity problems in online assessments. Remote proctoring watches the candidate and environment during the test; a smart browser locks down the machine so the candidate can't reach the rest of the internet in the first place. Most teams treating remote proctoring vs smart browser as an either/or are asking the wrong question — the honest answer is which layers you need, and where each one fails.

This piece is written for recruiters and hiring teams running technical assessments at scale. If you're running certification exams or high-stakes academic testing, the trade-offs shift, and we'll flag where.

What remote proctoring actually does

Remote proctoring is the monitoring layer. It uses the candidate's webcam, microphone, and screen feed to detect behaviors that suggest cheating — a second person in the room, a phone off-camera, eyes moving toward a second screen, or the browser losing focus.

There are three common modes:

  • Live proctoring: a human watches in real time, one-to-one or one-to-many. Highest signal, highest cost. Per-candidate live proctoring rates reported publicly typically fall in the low tens of dollars per hour, though pricing varies significantly by volume, vendor, and region.
  • Recorded proctoring: the session is captured and reviewed after the fact, either by a human or by an automated flagging system that surfaces incidents for review.
  • Automated proctoring: software flags anomalies in real time — face not detected, multiple faces, tab switching, unusual audio — without a human in the loop. Some vendors also layer real-time human intervention on top of automated flags, where a live proctor is pulled in only when the software surfaces a suspicious event; this hybrid mode aims to combine scale with human judgment.

Remote proctoring catches the things that happen around the test: a second person coaching, a phone under the desk, an identity mismatch between the person who registered and the person taking the exam.

Where it misses: anything the camera can't see. A candidate reading from a paper taped just below webcam frame. A smartwatch. A whispered assist from someone outside audio range. Historical reporting on remote proctoring from 2020 suggested that even at scale, real-time human proctors flag only a portion of incidents that post-hoc review later surfaces — and post-hoc review itself only catches a portion of what actually occurs.

The bigger miss is philosophical. Remote proctoring assumes the candidate's local machine is a trustworthy surface. It's not. If a candidate can alt-tab to ChatGPT in a second window, the webcam won't help.

What a smart browser actually does

A smart browser is the lockdown layer. It's a controlled environment — usually a dedicated desktop application or hardened web runtime — that restricts what the candidate can do on their own machine during the assessment.

A well-designed smart browser typically prevents:

  • Switching to other applications or tabs
  • Copy-paste from external sources
  • Opening a second monitor or extending the display via HDMI or other display outputs
  • Taking screenshots or screen recording
  • Running virtual machines or remote desktop sessions
  • Access to browser extensions, including AI assistants

HackerEarth's Smart Browser, for context, enforces these controls alongside the assessment session and surfaces violation attempts to reviewers for post-assessment audit. Similar lockdown capabilities exist across the category from a range of assessment vendors — the underlying approach is not unique to any one platform.

Where a smart browser catches what proctoring misses: it removes the ability to reach ChatGPT, Stack Overflow, or a co-worker on Slack in the first place. For a technical assessment, this is the higher-leverage control. You don't need to detect the tab switch if the tab switch can't happen.

Where a smart browser misses: anything happening off the monitored machine. A phone in the candidate's lap. A printout. A second laptop borrowed from a friend. A person whispering answers from behind the webcam.

There's also a real cost to candidate experience. Smart browsers require installation, they consume system permissions candidates are (rightly) cautious about granting, and they fail more often on unusual hardware. A small share of candidates will hit setup friction — build a support path for it.

Remote proctoring vs smart browser: they fail in opposite directions

The frame we prefer: remote proctoring monitors the human, a smart browser controls the machine. They fail in opposite directions.

Threat Remote proctoring catches it Smart browser catches it
Second tab open to ChatGPT Sometimes, via tab-switch or focus-loss detection (varies by vendor) Yes (blocks outright)
Second person in the room Yes (video/audio) No
Phone off-camera Rarely No
Copy-paste from Stack Overflow Sometimes Yes
Identity substitution (proxy candidate) Yes (ID check + face match) No
Screen sharing to a helper Sometimes Yes (blocks)
Notes taped below the webcam Rarely No
Virtual machine or remote desktop Sometimes Yes
Second monitor via HDMI or extended display Sometimes, if display config is checked Yes (blocks extended displays)

Neither is complete on its own. For a technical assessment specifically — where the highest-leverage cheat is reaching an AI model or a code-answer site — the smart browser blocks the more common failure mode. For an assessment where identity fraud or environmental coaching is the higher risk, remote proctoring does more work.

For high-stakes hiring — senior engineering roles, roles with confidential IP exposure — a defensible approach is to combine both, plus a downstream interview stage that re-tests the same skills live. Any single layer will miss determined cheating.

Remote proctoring vs smart browser in an AI-assisted world

The rise of coding-capable LLMs has moved the goalposts. Prior to widespread LLM adoption, the dominant cheat on a technical screen was Googling. Today it's pasting the prompt into Claude or ChatGPT and getting a working solution in seconds. Recent industry reporting on AI-assisted cheating in technical assessments consistently points to the same pattern: candidates increasingly reach for a model, not a search engine.

This matters for the remote proctoring vs smart browser choice because:

  • Remote proctoring's tab-switch detection is now the front line, and it's imperfect. Candidates using a second device (phone, tablet, second laptop) don't switch tabs at all. The webcam may or may not catch it.
  • Smart browsers are more effective against LLM-assisted cheating on the primary machine because they close the fastest path. But they don't stop a second device.
  • Take-home assignments are increasingly hard to defend as a sole signal, because the AI-assist question is unanswerable at home. Take-home work still has a role — as calibration, or as a starting point for a live discussion — but not as the only gate.

The realistic answer for teams hiring engineers today: assume some candidates will use AI. Design assessments that make AI use either detectable, permitted-and-scored, or structurally unhelpful (live problem-solving with follow-up questions is the third path). HackerEarth Assessments pairs smart-browser lockdown with skill-based question design intended to make AI-assisted answers easier to spot on review.

Dominant Cheating Method on Technical Assessments: Then vs Now
Source: Illustrative based on article claims about shift from Googling to LLM-assisted cheating over two years

How to choose the right integrity layer

Start with the question you're actually trying to answer:

If the risk is candidates accessing AI or external code during a technical test: the smart browser does more work than remote proctoring. Add basic automated proctoring for identity verification and belt-and-braces coverage. Live human proctoring is overkill here.

If the risk is proxy candidates — someone other than the applicant taking the test: you need identity verification, ideally KYC-grade. A smart browser alone won't catch this. Remote proctoring with ID check, or a dedicated interview-stage verification layer like HackerEarth's OnScreen AI interview — which provides KYC-grade identity verification at the live interview stage rather than wrapping the screening assessment itself — addresses proxy risk more directly.

If the risk is a coached environment — a candidate with a helper off-camera: live human proctoring is the highest-signal option. It's also the most expensive and the least scalable. For most hiring, a follow-up live technical round with an engineer serves the same function at lower cost per candidate.

If you're running high-volume campus or entry-level hiring: the economics push toward smart browser + automated proctoring. Live proctoring at 10,000+ candidates per season is prohibitive, and the marginal signal per dollar drops fast. Pair with a live technical round only for shortlisted candidates.

If you're running senior technical hiring: the assessment is one signal among several. Spend less energy on assessment-stage proctoring and more on rubric-based live interviews. A determined senior candidate will defeat any single-layer control; the defense is the interview, not the lockdown.

Two more principles worth stating plainly. First, transparency matters. Candidates who know what's being monitored and why complete more assessments and complain less. Bury the proctoring disclosure and you'll see drop-off and Glassdoor reviews. Second, log everything and review a sample. Even a smart-browser-plus-proctoring stack fails silently if no one ever audits the flagged sessions.

Frequently asked questions

Can Proctorio detect cheating? Proctorio and other automated proctoring tools in the same category detect a defined set of signals: face presence, multiple faces, gaze direction, tab or window focus loss, and audio anomalies. They can surface behaviors that correlate with cheating, but they don't "detect cheating" in a definitive sense — they generate flags for human review. Detection quality varies by lighting, hardware, and candidate environment, and none of these tools see off-device activity like a phone in the candidate's lap.

Does smart proctoring record you? Yes, in most implementations. Automated and recorded proctoring modes capture webcam video, microphone audio, and screen video for the duration of the session, and store them for post-assessment review. Smart browsers, on their own, typically do not record webcam or audio — they enforce environment controls on the machine and log violation events. When smart browser and proctoring are used together, the session is recorded. Candidates should be told this explicitly before they accept the test invite.

Can remote proctoring detect screen mirroring, a second monitor, or an HDMI output? Some can, some can't. Vendors that check display configuration at session start (looking for extended displays, HDMI or other external outputs, or unusual resolution changes) catch obvious cases. A candidate using a physically separate device — a phone, a second laptop — is invisible to the proctoring software regardless of vendor. Smart browsers typically block extended displays outright. This is a common gap and is worth confirming with any vendor before signing.

Can online exams detect cheating, including phone use? Partially. Online exams can detect on-device behaviors (tab switching, copy-paste, extension use, extended displays) reliably, and can detect some off-device behaviors (a second face in frame, off-screen voices, eye movement patterns) through webcam and mic analysis. Phone use specifically is one of the hardest signals to catch: a phone held below the desk, out of webcam frame, is invisible to almost every consumer-grade proctoring setup. Room scans at session start help but don't cover mid-test phone use. This is a known gap across the category, not a fixable flaw of any one tool.

Is a smart browser enough on its own for a technical assessment? For most first-round technical screens, yes — provided you pair it with identity verification and a follow-up live round for shortlisted candidates. A smart browser closes the highest-leverage cheat path (AI access on the test machine). It doesn't stop proxy candidates or coached environments, which is why the live round matters.

Do smart browsers work on all candidate devices? No. Most enforce minimum OS versions, block virtualized environments, and require specific browser or app installation. A small share of candidates will hit setup friction, and the rate is higher on older or corporate-locked machines. Have a support path — either a live-proctored alternate flow or a scheduled retest — before rolling out mandatory smart-browser assessments at scale.

Are AI-based proctoring flags reliable enough to act on? Not on their own. Automated flags are useful for surfacing sessions worth reviewing, not for rejection decisions. Reporting from the Electronic Frontier Foundation during the 2020–2021 remote-testing wave documented meaningful false-positive rates that hit candidates of color and neurodivergent candidates disproportionately. That data is now several years old and reflects the state of the tools at that time, but the underlying pattern — automated flags require human review — remains a widely held view. Treat flags as input to human review, not as verdicts.

Does adding proctoring hurt candidate completion rates? It can, especially if disclosure is unclear or the setup is heavy. Communicating what's monitored, why, and what happens to the recording — before the candidate accepts the test invite — reduces drop-off. Silent surveillance produces the worst outcomes on both integrity and candidate experience.

Key takeaways

  • Remote proctoring monitors the human; a smart browser controls the machine. They fail in opposite directions and work best in combination.
  • For technical assessments where AI access is the primary risk, a smart browser does more work per dollar than live human proctoring.
  • Identity verification is a separate problem from cheating detection — solve it explicitly, not by assuming proctoring covers it.
  • No single integrity layer is defensible for high-stakes hiring; the follow-up live technical round is where senior hires are actually calibrated.
  • Automated proctoring flags belong in human review queues, not in automated rejection logic.

Next steps

If you're rebuilding your assessment integrity stack, start with the threat model, not the vendor demo. Map which cheats you're actually seeing in your pipeline, then match layers to threats. To see how smart-browser lockdown and AI-driven interview verification work together in practice, book a walkthrough of HackerEarth Assessments.

Workforce Skills Audit for AI Transformation: A Guide

Meta title: Workforce Skills Audit for AI Transformation: A Practical Guide Meta description: Learn how to conduct a workforce skills audit before an AI transformation program — with steps, metrics, and pitfalls to avoid. Read the guide.

How to Conduct a Workforce Skills Audit Before an AI Transformation Program

11 min read

The gap between AI license spend and AI-driven productivity is now wide enough that boards are asking CHROs to explain it — and the honest answer usually starts with the fact that no one measured workforce readiness before signing the contract. A workforce skills audit before an AI transformation program is the diagnostic step that separates companies making informed capability investments from companies buying enterprise licenses that gather dust. The audit's most underrated output is not the skills map itself but the employee trust and change-management foundation it builds — a differentiator this guide surfaces up front rather than as an afterthought.

Done well, a workforce skills audit before an AI transformation program produces a clear map of who can already work with AI tools, who needs targeted upskilling, and which roles will change shape entirely. This guide walks through the steps, the metrics that matter, and the trade-offs most rollouts ignore. It is written for CHROs, Heads of People Analytics, and Heads of L&D who have been asked, usually by the board, how AI-ready their workforce is and don't yet have a clear answer.

The competitive angle most audits miss: employee trust and change management

Before the first assessment goes out, consider the employee experience. Skills audits can trigger surveillance anxiety, especially when framed poorly or when results are perceived as inputs to workforce reduction. Most published guides treat this as a footnote; in practice, it is the variable that most consistently predicts whether an audit produces usable data or shelf-ware. A few considerations worth building into the program design:

  • Communicate purpose up front. Employees are more likely to engage honestly with assessments when the audit is framed as an input to development and mobility, not evaluation for cuts.
  • Data protection and legal scope. In GDPR jurisdictions and where works councils or unions are active, assessment data is subject to consultation requirements and clear retention rules. Loop in legal and employee relations before, not after.
  • Anonymised aggregate reporting. Individual-level results should stay with the employee and their manager; leadership and board reporting should be at the cohort level.
  • Right to challenge results. Any validated assessment can misfire. Employees should have a clear route to contest or retake, particularly where results feed into role changes.

Published enterprise AI adoption post-mortems consistently note that audits without a communications plan produce lower participation and lower trust in the resulting training programs.

Why a workforce skills audit matters before AI transformation

An AI transformation program without a skills audit is a procurement exercise. You buy Copilot seats, roll out a GenAI policy, and hope adoption follows. It rarely does. A 2024 BCG study of workers across multiple countries reportedly found that regular use of GenAI among frontline employees has grown sharply year over year, while only a minority had received formal training on the tools. BCG has also reported that untrained users are less likely to trust or effectively use AI. Readers should consult the report directly for the exact percentages, as figures have been revised across BCG's series.

The audit isn't about counting who has "AI skills." It's about answering three questions with evidence:

  • Where in the workflow does AI actually change the work?
  • Which people can already do that work, and which cannot?
  • What is the shortest path from the current state to an AI-fluent workforce?

Skip this and you get a pattern documented in MIT Sloan's coverage of enterprise AI adoption: enterprises investing in AI without precise insight into current workforce skills end up with adoption concentrated among the already-fluent and abandoned by everyone else. Closing skills gaps requires precise measurement first, not blanket training programs.

What a workforce skills audit for AI transformation actually measures

A traditional skills audit inventories capabilities against role descriptions. A workforce skills audit for AI transformation adds three layers that a traditional audit misses.

Task-level exposure to AI. The question is not "does this person know Python." It is how much of this person's weekly work is automatable, augmentable, or unchanged by current generative AI tools. The OECD Employment Outlook 2023 discusses AI's impact at the level of tasks within occupations rather than occupations as a whole. A task-level view is the one that most directly drives training decisions.

AI-collaboration skill, not AI-tool literacy. Knowing how to open ChatGPT is not a skill. Being able to write a prompt that produces production-ready output, evaluate the output for hallucination or bias, and integrate it into a defensible workflow — that is a skill, and it varies wildly across the workforce.

Judgment and domain depth. The counterintuitive finding across most enterprise AI rollouts: the people who benefit most from AI tools are often the domain experts who can spot when the output is wrong. The audit needs to capture domain depth, not just tool familiarity.

The five steps to conduct a workforce skills audit before an AI transformation program

The steps below assume you have a workforce of at least 1,000 employees. At smaller scale, most of the same principles apply but you can compress the process into weeks rather than months.

Step 1: Translate the AI transformation strategy into audit objectives

Before measuring anything, name the business outcomes the AI program is meant to deliver. "Improve productivity" is not an objective. "Reduce time-to-resolution in customer support by 30% using AI-assisted response drafting" is. Every skill you audit should map back to at least one named outcome.

This step also functions as an intake exercise for the audit itself. Before you commission any assessment, work through a short intake questionnaire with the executive sponsor. A condensed example:

  • Role and function in scope. Which functions are we auditing, and why these first?
  • Industry and regulatory context. Are there compliance constraints (financial services, healthcare, EU AI Act exposure) that shape what "AI-ready" means here?
  • Success definition. What does high performance look like in each in-scope role 12 months after the AI rollout — in observable terms?
  • Existing data. What performance, LMS, or assessment data already exists that we should reuse rather than re-collect?
  • Constraints. Works council, union, or GDPR consultation requirements? Budget envelope? Timeline pressure from the board?

This step usually reveals that the AI program itself is under-specified. That is useful information — better to surface it now than after 18 months of training spend.

Step 2: Build the task-and-skill inventory

For the roles in scope, decompose the work into tasks and map each task to the underlying skills. Two shortcuts save weeks of effort:

  • Use an existing skills taxonomy as a starting point (SFIA for technical roles, WEF Future of Jobs taxonomies for cross-functional). Do not build one from scratch unless you have a reason.
  • Anchor the inventory in what people actually do, not in job descriptions. Job descriptions in most enterprises are 3–5 years out of date.

For each task, tag it with the AI-exposure layer: automatable today, augmentable today, augmentable within 12–24 months, or unchanged. This tag is what turns a skills inventory into an AI-readiness inventory.

Step 3: Measure current skills against the inventory

This is where most audits break down. Manager-reported and self-reported skills data is unreliable. Research from the World Economic Forum's Future of Jobs Report 2025 and academic work on skill self-assessment consistently show meaningful divergence between perceived proficiency and validated results. Treat the direction of that finding as a planning assumption rather than a single fixed benchmark.

Three measurement approaches work in combination:

  1. Validated assessments for skills where objective evaluation is possible — coding, data analysis, prompt engineering, structured problem-solving. Platforms like HackerEarth Assessments produce rubric-scored signal at scale for these skills, and their real-time skill intelligence output is what turns raw scores into a coverage map decision-makers can act on. For enterprises building internal AI-fluency programs, HackerEarth's VibeCode Arena adds a targeted evaluation of AI-collaboration behaviour — how a candidate or employee frames a prompt, iterates with an AI assistant, and validates the output — as a complement, not a replacement, to a broader assessment layer.
  2. Work-sample review for skills that don't compress into a test — writing, design judgment, client conversation. Look at recent artifacts, not hypothetical performance.
  3. Manager and self-assessment as triangulation, not ground truth. Where these three diverge sharply, that is a data point worth investigating.

Cover the workforce in tiers. Full assessment for the 15–25% of roles most exposed to AI change; sampled assessment for the middle tier; lightweight self-report with spot-check for the least exposed.

Sample rubric: a lightweight AI-collaboration self-assessment

Use this as a starting point for the self-report layer or as a manager conversation guide. It is not a replacement for validated assessment, but it surfaces the right conversation before you invest in one.

Dimension Level 1 — Aware Level 2 — Applied Level 3 — Fluent Level 4 — Coaching others
Prompt design Can use pre-written prompts Adapts prompts for own tasks Designs multi-step prompts with context Trains team on prompt patterns
Output evaluation Accepts output as-is Spots obvious errors Detects hallucination and bias reliably Sets team review standards
Workflow integration One-off use Uses AI in a recurring task Redesigns a workflow around AI Redesigns team workflows
Domain judgment Defers to AI output Cross-checks against domain knowledge Consistently improves AI output with domain expertise Mentors others on when to override

A completed row per employee, aggregated by team, produces a first-pass heat map before any formal assessment runs.

Step 4: Map gaps to actions with the Build / Buy / Borrow / Bridge framework

For each skill gap, decide which of four actions applies:

  • Build: targeted upskilling with a defined outcome and measurement. Not "complete a course" — demonstrate the skill.
  • Buy: hire for the gap. Often the right answer for scarce senior AI-native roles.
  • Borrow: contract or partner for time-limited need. Useful for capabilities you don't want to maintain internally.
  • Bridge: internal mobility. Move people from adjacent roles where their existing skills plus targeted training makes them AI-fluent faster than hiring externally.

Most enterprises over-index on Build and under-invest in Bridge. Bridge is where internal talent marketplaces produce the clearest ROI, and where a skills-based mobility approach shows results earliest.

Example: workforce skills audit at a mid-market insurer

A mid-market insurer with 4,000 employees audits its claims operations function. Task-level tagging identifies that 35% of adjuster tasks are augmentable with current GenAI tools. Validated assessment shows 20% of adjusters already operate at Level 3 on the rubric above, 55% at Level 2, and 25% at Level 1. The gap plan looks like this:

  • Build: structured upskilling for the 55% at Level 2, targeting Level 3 within 6 months on prompt design and output evaluation.
  • Buy: two senior AI-literate claims leads to seed the team.
  • Borrow: a 6-month vendor engagement to stand up prompt libraries and evaluation standards.
  • Bridge: move 15 high-performing customer service reps into adjuster tracks, where their existing domain exposure plus AI-collaboration training closes the gap faster than external hiring.

That single page — with skill levels, headcount, and named actions — is the audit output the board actually needs.

AI-Collaboration Proficiency Distribution: Claims Adjusters Before Audit Intervention
Source: Worked example, article (mid-market insurer case)

Step 5: Baseline metrics and set the re-audit cadence

The audit is not a one-time event. In HackerEarth's enterprise program experience, AI model capabilities in enterprise-relevant workflows appear to shift on a roughly 6–12 month cycle, based on observed vendor release patterns and customer adoption reporting. A skills baseline established today is partially stale within a year. Establish:

  • The metrics you will re-measure (skill coverage rate, AI-collaboration proficiency distribution, gap-to-target ratio by function)
  • The cadence — annually at minimum, semi-annually for roles at the frontier of AI exposure
  • The threshold that triggers action between audits (e.g., a new model capability that changes the exposure tag on a major task cluster)

Manager and employee interview guide

Assessment data alone does not tell you why a gap exists. A short structured interview — 20–30 minutes per participant on a sampled basis — turns rubric scores into a diagnosis. Use variants of the following prompts:

For managers:

  • Walk me through a recent task on your team where an AI tool was used well. What made it work?
  • Walk me through one where the output was wrong or unusable. How did you catch it?
  • Which two or three people on your team would you trust to redesign a workflow around AI, and why?
  • Where would you invest one week of training time for the whole team if that was all you got?

For employees:

  • Which parts of your weekly work do you already do faster or better with AI assistance?
  • Where have you tried AI and gone back to doing it the old way? Why?
  • What would need to change — tools, permissions, training, examples — for you to use AI on more of your work?
  • What is the one thing you would not want AI to do in your role, and why?

The pattern that emerges from these interviews, when triangulated with assessment data and manager rating, is usually a more accurate picture than any single measurement stream.

Using AI to conduct the skills audit itself

Published research from MIT Sloan and other enterprise AI adoption post-mortems covers how AI tools themselves can accelerate the audit. It is worth spelling out where AI helps and where it does not.

Where AI helps:

  • Role and task parsing. Feed job descriptions and JIRA/ticket histories into an LLM to extract task inventories at scale. This turns weeks of interview work into days of review work.
  • Skill clustering. Use embeddings to group related skills across taxonomies and reconcile inconsistent naming across functions.
  • Outlier detection. AI is good at flagging assessment results that diverge sharply from manager rating, tenure, or peer distribution — useful for prioritising manual review.
  • Draft development plans. Generate first-pass upskilling plans per employee that a manager then edits, rather than writing from scratch.

Where AI does not help (yet):

  • Primary evaluation of individual skill. LLM-based skill inference from resumes or activity logs produces high false-positive rates. Use it to prioritise, not to score.
  • Judgment-heavy skills. AI cannot yet reliably distinguish good domain judgment from confident-sounding output. Human review remains the anchor.
  • Bias-sensitive decisions. Anything that feeds into promotion, pay, or reduction decisions needs human-in-the-loop and auditable rubrics.

The practical pattern: use AI to accelerate the audit's process, use validated assessment for the evaluation itself, and use human review at every decision point that affects a person's role.

Common failure modes when conducting a workforce skills audit before AI transformation

Four patterns commonly documented in enterprise AI rollout post-mortems explain most failed audits.

Auditing tools instead of skills. "How many people have used ChatGPT this month" is a usage metric, not a skills metric. Usage without proficiency is noise.

Ignoring the domain-expert paradox. Senior domain experts often score low on AI-tool proficiency and high on AI-augmented output quality. If your audit metric is tool proficiency alone, you will misdirect training budget toward people who don't need it.

Building the taxonomy in a vacuum. HR-built skills taxonomies that never touch the actual workflow produce inventories that managers refuse to use. Every skill definition should be reviewed by someone who does the work.

Treating the audit as a compliance exercise. If the audit output is a slide deck for the board and nothing else, the money was wasted. The output is a training plan, a hiring plan, and a mobility plan with named individuals and measurable outcomes.

What good looks like: planning benchmarks

The figures below are HackerEarth's internal planning estimates from enterprise program experience, not audited public benchmarks. Treat them as directional inputs to your own budget and timeline conversations, and pressure-test them against your own vendor quotes and historical data.

  • Coverage. A well-run audit at enterprise scale typically covers 60–80% of in-scope roles with validated assessment within 90–120 days of kickoff.
  • Assessment layer cost. A rough working range of $40–120 per employee is a reasonable planning figure, with the higher end applying when custom role-based content is required.

Two outcome metrics matter more than the rest: the percentage of the workforce that moves at least one proficiency level on priority skills within 12 months of the audit, and the percentage of in-scope roles that hit their AI-augmented productivity target. If both are trending up, the audit did its job.

Frequently asked questions

Where do most audits break down in practice — and how do you catch it early? The single most common failure point is not the five-step process itself but the sequencing of stakeholder buy-in. Audits that start with HR building a taxonomy and only involve line managers at the assessment stage tend to produce inventories managers reject. The counterintuitive fix: involve two or three sceptical line managers in Step 2 (task inventory) before HR has committed to a taxonomy. If they cannot recognise the tasks their own team performs in the draft, restart Step 2 before spending on assessment.

What skills are required for AI transformation? At the workforce level, four skill clusters matter: AI-collaboration skills (prompt design, output evaluation, workflow integration), data literacy, domain judgment, and change adaptability. Technical AI skills (ML engineering, model fine-tuning) matter for a small specialist cohort. The distribution across these clusters varies by role — a customer support agent needs different AI skills than a data analyst.

How long does a workforce skills audit for AI transformation take? For a 1,000–10,000-person workforce, plan for 90–120 days from kickoff to actionable output, assuming an existing skills taxonomy is used as the starting point. Building a taxonomy from scratch adds 60–90 days. Larger enterprises typically phase by function rather than attempting a single-wave audit.

Should we use AI to conduct the skills audit itself? Partially. See the "Using AI to conduct the skills audit itself" section above for a detailed breakdown of where LLMs and embeddings accelerate the process and where they should not be the primary signal.

What is the hardest audit trade-off no one talks about? The tension between assessment depth and employee trust. The more rigorous the validated assessment, the more it feels like surveillance to employees — and the more likely participation drops or is gamed. The organisations that resolve this well tend to invest disproportionately in the communications wrapper (purpose, data handling, right to challenge, individual data ownership) before the assessment goes out, not after. If your program plan spends more on the assessment vendor than on the change and communications workstream, that is usually a warning sign.

Can smaller companies conduct a meaningful skills audit before AI transformation? Yes, at compressed scope. Under 500 employees, focus on the 10–20 roles most exposed to AI change, use lightweight validated assessment for those roles, and rely on manager conversation for the rest. The five-step structure still applies; the timeline compresses to 4–6 weeks.

Key takeaways

  • Conduct the audit before buying AI tools at scale — procurement without capability data produces low adoption and stranded license spend.
  • Measure task-level AI exposure and AI-collaboration skill, not tool usage or self-reported familiarity.
  • Combine validated assessment, work-sample review, and self-report as triangulation — never rely on self-report alone.
  • Map every gap to Build, Buy, Borrow, or Bridge; most enterprises under-invest in Bridge and over-invest in Build.
  • Treat the audit as a recurring baseline on a 6–12 month cadence, not a one-time deliverable.
  • Design the audit with employee trust and data protection in mind from day one, not as an afterthought.

Next steps

To see how validated skill assessment fits into an AI-readiness audit at enterprise scale, request a walkthrough of HackerEarth Assessments. To go deeper on the mobility side of the Build/Buy/Borrow/Bridge framework, read how skills-based hiring rollouts succeed and fail, or explore HackerEarth's technical hiring blog for related program design guides.

How to Run a Panel Interview That Gets a Decision

Meta title: How to run a panel interview that produces a decision Meta description: How to run a panel interview that produces a decision, not a debate — a practical guide to structure, rubrics, and debrief that actually close roles.

How to run a panel interview that produces a decision, not a debate

A panel interview is a hiring session in which multiple interviewers evaluate the same candidate against a shared rubric, then reconcile their independent judgments into a single decision. To run one that produces a decision rather than a debate, assign each panelist a specific competency to evaluate, require independent written scorecards before any group discussion, and structure the debrief to focus only on scoring disagreements.

Learning how to run a panel interview that produces a decision, not a debate, starts with accepting that panels don't fail during the interview. They fail in the 20 minutes after — when four people who watched the same candidate walk out with four different conclusions and no way to reconcile them. If your panels regularly end in a Slack thread that stretches for three days, the interview isn't the problem. The debrief structure is.

Most guides on how to run a panel interview treat the session itself as the event. That's backwards. The session is a data-collection exercise. The decision is a separate exercise, and it needs its own rules. Research on structured interviewing consistently shows it outperforms unstructured formats on predictive validity — but only when the structure extends into how the panel makes its decision.

Why panel interviews turn into debates

Panels debate for three reasons, and they're almost never about the candidate.

The first is coverage overlap. Two interviewers ask about system design. Both form opinions. Neither has data on how the candidate handles ambiguity, code quality, or collaboration — because no one was assigned to look for it. In the debrief, the two design interviewers argue with each other while the actual gaps go undiscussed.

The second is rubric drift. The team agreed on a scoring guide six months ago. Since then, two interviewers have started weighing "communication" more heavily, one has quietly stopped caring about testing, and the newest panelist is calibrating against their last company's bar. Same rubric, five interpretations. If you don't already have a shared scoring language, our guide on designing interview rubrics that reduce bias is a useful starting point.

The third is timing. When interviewers submit scorecards after the debrief starts — or worse, during it — the loudest voice in the room anchors the discussion. Everyone else adjusts to fit. This is well-documented in decision science. Research on group polarization — including work by Cass Sunstein at Harvard Law School in Wiser: Getting Beyond Groupthink to Make Groups Smarter (2015) — suggests that groups amplify errors when members share opinions before independent judgment is captured, a dynamic that plausibly applies to hiring panels.

The pre-panel work that makes running a panel interview possible

Before the interview happens, three things need to be locked. Skip any of them and you're building the debate you're trying to avoid.

Assign coverage explicitly. Each panelist gets one or two competencies to evaluate — coding, system design, debugging, cross-functional collaboration, whatever the rubric names. No two panelists cover the same thing. If your rubric has six dimensions and your panel has four people, some dimensions get double-coverage and some get one owner. Decide which before the loop starts, not after.

Calibrate the rubric on a real example. Take a scorecard from a recent hire — ideally one where the panel disagreed — and have the current interviewers score it independently. Then compare results. Where the scores diverge by more than one point on a five-point scale, you have a calibration gap. Fix the rubric language, not the interviewers. This takes an hour. Most teams don't do it, then spend that hour every week arguing in debriefs instead.

Set the scorecard deadline before the debrief. Every panelist submits their scorecard independently, in writing, within 24 hours of their interview and before the debrief begins. No exceptions. If a scorecard isn't in, the debrief doesn't start. This is the single highest-leverage rule in the process and the one most teams refuse to enforce.

How to run the panel interview itself

The interview is the easy part if the pre-work is done. A few operational rules make it easier.

Cap each session at 45 to 60 minutes. In practitioner experience, anything longer tends to correlate with fatigue rather than better signal. Keep transitions between interviewers under five minutes — long gaps degrade the candidate experience and give panelists time to compare notes, which contaminates independent judgment.

Interviewers should not attend each other's sessions unless the format explicitly requires it (a senior hire's system design round, for example, sometimes benefits from a silent observer). Otherwise, the observation becomes a discussion, and the discussion becomes the anchor.

Give the candidate one contact for logistics — usually the recruiter. Panelists focus on evaluation; coordination lives outside the panel. If your interview process still routes reschedules through the hiring manager, that's a workflow problem, not a panel problem. Tools like FaceCode enforce the independent-scorecard rule by storing each interviewer's scores against the rubric before the debrief begins, so the loop lead can see at a glance who has submitted and block the debrief from starting until every panelist is in. That doesn't fix an uncalibrated rubric, but it removes the most common excuse for skipping the rule.

The debrief structure that produces a decision

Here is where most panels lose the plot. This is the part of how to run a panel interview that most teams get wrong. The debrief is not a discussion. It's a structured decision meeting with a specific sequence.

Step one: read the scorecards silently. Everyone opens the submitted scores and comments. No talking for the first five minutes. This forces every panelist to encounter the others' reasoning before hearing their tone.

Step two: identify the disagreements, not the agreements. The hiring manager or loop lead names the specific rubric dimensions where scores diverge by more than one point. Those are the only items discussed. If four panelists gave the candidate a 4 on coding, don't spend 10 minutes agreeing about it.

Step three: each disagreement gets a five-minute cap. The two panelists with divergent scores present their evidence — what the candidate said, what they did, what the rubric asks for. Other panelists ask questions. No new scores are assigned; the goal is to surface what the disagreement is actually about. In our observation across structured debriefs we've seen, a large share of "disagreements" — often the majority — collapse in under two minutes once both sides describe what they saw. They were evaluating different things.

Step four: the hiring manager makes the call. Panel input is data. The hiring manager owns the decision. This is not a democracy, and pretending it is produces the drawn-out debates that panels are famous for. If the hiring manager overrides a strong dissent, they document why. That documentation matters for future calibration and, in regulated industries, for defensibility. SHRM's guidance on structured hiring decisions reinforces the value of documented rationale for later review.

The whole debrief should take 30 to 45 minutes. If yours regularly runs longer, the pre-work is broken.

Share of Debrief Disagreements That Collapse Within 2 Minutes
Source: Based on article claims

What to do when the panel is genuinely split

Sometimes the disagreement is real. Two experienced engineers watched the same candidate solve the same problem and reached opposite conclusions about whether the candidate can handle the role. That's a signal, not a bug.

The default move in most companies is to add another round. This is usually wrong. Adding a round rewards the loudest dissenter and punishes the candidate for a process failure. It also signals to the panel that disagreement gets resolved by more interviewing, which encourages performative doubt in future loops.

A better move: name the specific competency in dispute, and design a 30-minute targeted follow-up focused only on that dimension. If two panelists disagree about the candidate's ability to debug production issues, run a debugging exercise. Don't run another general interview. This respects the candidate's time and produces evaluable data on the actual disagreement.

If the split is about seniority rather than skill — the candidate can do the job but not at the level being hired for — that's a leveling conversation, not a hiring decision. Loop the recruiter in to renegotiate the offer level with the candidate before rejecting.

Trade-offs worth naming when you run a panel interview this way

Structured panels give up some things. Serendipity is one — the moment where a candidate mentions a project that unlocks a completely different role fit. Rigid coverage assignments make those moments less likely. Build in a five-minute open-question slot per interview if that matters to you.

Structured panels can also feel bureaucratic to interviewers who take pride in "reading" candidates. That instinct is real, and sometimes right, but it's also where most bias enters the process. If your interviewers resist calibration because it constrains their judgment, that resistance is exactly the reason to do it.

Finally, structured debriefs put more work on the hiring manager. They have to run the meeting, own the decision, and document overrides. If your hiring managers won't do this, no interview format will save you. That's a management problem, not a process one.

Frequently asked questions

How many people should be on a panel interview?

A common practitioner recommendation is three to five, with four as a frequent default. Fewer than three concentrates decision weight on one or two people. More than five produces coverage overlap and slower debriefs without meaningfully better signal. Senior hires sometimes justify a fifth or sixth panelist for a specific competency, but that panelist should have a named coverage area, not a floating observer role.

Should the hiring manager be on the panel?

Yes, but not as the deciding voice inside the panel. The hiring manager interviews for their own rubric dimension, submits a scorecard like everyone else, and then runs the debrief as decision-owner. Conflating panelist and decision-maker inside the panel session is what produces the anchoring problem — everyone else calibrates to the hiring manager in real time.

How do we prevent one senior panelist from dominating the debrief?

Silent scorecard review first, then discuss only disagreements, then five-minute caps per disputed dimension. The structure does the work. If a senior panelist still dominates, the hiring manager needs to actively redirect — "we've heard your view on this dimension; let's hear from the other interviewers." If they won't do that, the debrief structure isn't the fix.

What if the candidate performs differently across interviewers?

Inconsistent performance across interviewers most often signals a calibration problem, not a candidate problem — the panel isn't asking comparable questions or applying comparable rubrics. Occasionally it reflects real candidate variability under different interviewer styles, which is worth knowing. Name the pattern in the debrief: "Interviewer A saw strong debugging, Interviewer B saw hesitation. What was different about the two sessions?" That question usually surfaces the actual issue.

How long should the full panel loop take?

For most engineering roles, four interviews of 45 to 60 minutes plus a 30-minute debrief — so a same-day loop of four to five hours, or a distributed loop over two to three days. Practitioner experience suggests that loops longer than six total interview hours tend to correlate with candidate drop-off rather than better decisions.

Panel Loop Length vs. Candidate Drop-Off Risk
Source: Based on article claims

Key takeaways

  • Panel debates are usually caused by unassigned coverage, uncalibrated rubrics, and scorecards submitted after discussion starts — fix those first.
  • Independent, written scorecards submitted before the debrief are the single highest-leverage rule; refuse to start the debrief without them.
  • Debriefs should discuss disagreements only, cap each disputed dimension at five minutes, and end with the hiring manager owning the decision.
  • When panels genuinely split, run a targeted 30-minute follow-up on the specific competency in dispute — not another full round.
  • Structured panels trade serendipity for consistency; make the trade deliberately, and document override decisions for calibration and defensibility.

See it in action

If your panels are producing debates instead of decisions, the fastest audit is to pull the last 10 loops and count how many had all scorecards submitted before the debrief started. If it's fewer than eight, start there. For teams looking to standardize the interview session itself across distributed panels, take a look at how FaceCode structures multi-interviewer coding rounds or schedule a walkthrough of HackerEarth's assessment and interview stack.

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