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Introduction to Machine Learning Interviews Book
As a candidate, I’ve interviewed at a dozen big companies and startups. I’ve got offers for machine learning roles at companies including Google, NVIDIA, Snap, Netflix, Primer AI, and Snorkel AI. I’ve also been rejected at many other companies.
As an interviewer, I’ve been involved in designing and executing the hiring process at NVIDIA and Snorkel AI, having taken steps from cold emailing candidates whose work I love, screening resumes, doing exploratory and technical interviews, debating whether or not to hire a candidate, to trying to convince candidates to choose us over competitive offers.
As a friend and teacher, I’ve helped many friends and students prepare for their machine learning interviews at big companies and startups. I give them mock interviews and take notes of the process they went through as well as the questions they were asked.
I’ve also consulted several startups on their machine learning hiring pipelines. Hiring for machine learning roles turned out to be pretty difficult when you don’t already have a strong in-house machine learning team and process to help you evaluate candidates. As the use of machine learning in the industry is still pretty new, a lot of companies are still making it up as they go along, which doesn’t make it easier for candidates.
This book is the result of the collective wisdom of many people who have sat on both sides of the table and who have spent a lot of time thinking about the hiring process. It was written with candidates in mind, but hiring managers who saw the early drafts told me that they found it helpful to learn how other companies are hiring, and to rethink their own process.
The book consists of two parts. The first part provides an overview of the machine learning interview process, what types of machine learning roles are available, what skills each role requires, what kinds of questions are often asked, and how to prepare for them. This part also explains the interviewers’ mindset and what kind of signals they look for.
The second part consists of over 200 knowledge questions, each noted with its level of difficulty – interviews for more senior roles should expect harder questions – that cover important concepts and common misconceptions in machine learning.
After you’ve finished this book, you might want to checkout the 30 open-ended questions to test your ability to put together what you know to solve practical challenges. These questions test your problem-solving skills as well as the extent of your experiences in implementing and deploying machine learning models. Some companies call them machine learning systems design questions. Almost all companies I’ve talked to ask at least a question of this type in their interview process, and they are the questions that candidates often find to be the hardest.
“Machine learning systems design” is an intricate topic that merits its own book. To learn more about it, check out my course CS 329S: Machine learning systems design at Stanford.
This book is not a replacement to machine learning textbooks nor a shortcut to game the interviews. It’s a tool to consolidate your existing theoretical and practical knowledge in machine learning. The questions in this book can also help identify your blind/weak spots. Each topic is accompanied by resources that should help you strengthen your understanding of that topic.