Getting hired by Facebook

Working at Facebook – or any major tech company: How to Break In, and What to Expect on the Job

By Peter Harrison on 17th August 2018

At Peter Harrison Careers, our Tech Consultants have worked at many of the world’s leading tech companies. Below, one of our coaches shares his experiences of working at Facebook.

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1. Can you give me an overview of your area?

I work on technologies like Machine Learning, Deep learning, Statistics and Data Science. Most of the problems I work on are related to the field of Computer Vision and Language Modeling tasks. For example, Image Classification, video captioning, semantic segmentation of images and so on. All these topics/fields can be loosely referred to as Artificial Intelligence (“AI”). I have worked on some of the classical AI problem statements like computer vision for self-driving cars, roads and lanes detection on satellite imagery data, workload classification on medical sensory data (EEG, ECG), social network analysis and many more. 

2. Generally, what should you expect in the recruiting process from first contact to final round?

The hiring process for the role of any Machine learning engineer/researcher remains more or less similar to that of a software engineer. At least the initial few steps of screening remain the same – topics like data structures, analysis of algorithms, parallel programming, dynamic programming, designing a system, etc. will be tested. Once you clear these steps you then reach the intermediate stages where you can expect some Machine Learning-based questions, specifically relating to Math and Probability, gradually narrowing down to building models and analyzing them in detail. 

3. Specifically, what is the interview and assessment process like?

The process mainly involves technical rounds, with some of them being “white board interviews” (where you will be asked to derive a formula or write down an algorithm and then analyze its time and space complexity) and some just being a one-on-one with several managers/senior engineers in the company. Sometimes, the process can get intense where it may last for 3-4 hours without a break, while during other sessions it may be multiple sprints of 1-1.5 hours each. 

4. What got you hired? 

Well, I worked extremely hard on my basic concepts of programming (some of which I listed above). People usually assume that if you're applying for a Machine Learning or Data Science role you'll be asked questions from that specific domain. However, when you talk about companies like Facebook or Google, they don’t want domain-specific experts, they are looking out for smart programming guys who can excel at any programing task, be it a Machine Learning problem or a complex firmware issue. So, you have to understand your algorithms deep enough to explain every bit of them and, of course, the Machine Learning and AI understanding has to be there, without saying! 

5. What other teams or people do you interact with?

I worked in one of the research teams at Facebook where most of my work was on Machine Learning frameworks, building models, comparing performances, optimizing them for production and then deploying them. However, as everyone knows, Facebook does have an open culture and you get to meet all sort of smart minds throughout the company be it from marketing, designing or the legal team. 

6. How does advancement and promotion work?

Well, Facebook is one of those companies which pays you well – along with all those perks and awesome food! However, when it comes to advancement and promotions you are individually judged on your performance. The better you do on your assigned project, the higher are your chances of getting a promotion or a bonus every fiscal year. 

7. What are the exit opportunities?

If you're working for a company like Facebook, I don’t really think you would want an exit :P 

But sometimes it happens that you don’t like the work you’re doing and are not satisfied by your personal achievements even though the team may be making great progress. In that case, I would say you should take an exit and give yourself sometime for self-analysis – maybe try out different things in the same industry or perhaps another industry entirely. However, having worked at Facebook or at any company of that reputation, you develop a sense of credibility in the industry and it should be fairly easy to get another opportunity because of the work you’ve done and the company you're coming from. 

8. Who is good fit and who is bad fit for this role?

Anyone who understands tech, loves programming and is ready to take up a role outside of his/her comfort zone (if needed) is definitely a fit to work at Facebook. However, the rest should definitely work on their skills and then apply, since the interview process is lengthy and tiring – and if you end up with a rejection, knowing that you didn't prepare hard enough really sucks. So make sure you give your 100% and are ready to face whatever comes to get a seat in the spaceship!

9. How do you think this job will change in the future?

I see a lot of opening/opportunities for people working in the field of Machine Learning/AI to arise in the coming years. As we generate more and more data, we are shifting towards an era where we want more intelligent machines and system to take over the mundane tasks and also up to certain level some complex tasks. So there's definitely an increasing demand for ML/AI engineers and Data Scientists in the coming future. 

10. What can people read and study to gain an advantage in getting hired?

I would highly recommend reading classical Machine Learning books (e.g. “Introduction to Machine Learning” by Kevin Murphy) that would let you understand "why should you even use deep learning", before jumping to "how should you use deep learning". Apart from that, MOOCs are a very good source of information and learning these technologies. Develop a habit of reading a lot of research papers (because that is where the real meat exists) and building your models/writing your own algorithms from scratch. That's the best way of practicing things! 

Thanks for the great scoop!

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