chiphuyen/ml-interviews-book

This repository holds the source code for "Introduction to Machine Learning Interviews," a book by Chip Huyen aimed at people preparing for technical interviews at companies that hire machine learning engineers and researchers. A free web version of the book is available at the author's website, and the source lives here on GitHub. The author draws on experience from three angles: as a candidate who received offers from Google, NVIDIA, Snap, Netflix, and others while also being rejected at several places, as an interviewer who helped design hiring processes at NVIDIA and Snorkel AI, and as a mentor who has coached friends and students through mock interviews. The book grew out of notes taken during those coaching sessions and conversations with people on both sides of the hiring table. The book is divided into two parts. The first covers the structure of machine learning interview pipelines: what types of roles exist (research, applied, engineering), what skills each expects, what kinds of questions come up, and how interviewers evaluate candidates. The second part contains over 200 knowledge questions ranging across core machine learning concepts, with difficulty levels marked so candidates can gauge what to expect for more senior roles. Beyond those questions, the README points to a separate set of 30 open-ended machine learning systems design questions. These are scenario-based problems that test whether a candidate can apply their knowledge to practical challenges like designing a recommendation system or deciding how to deploy a model. The author notes these are often the questions candidates find hardest, and nearly every company includes at least one. The README is clear that the book is not a textbook replacement or an interview shortcut. It is meant to consolidate knowledge you already have and help you find gaps before an interview.