What is ml-interviews-book?

chiphuyen/ml-interviews-book — explained in plain English

Analysis updated 2026-06-26

★ 4,623HTMLAudience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((ml-interviews-book))
    What it is
      Free interview prep book
      Web version available
      Source on GitHub
    Author background
      Candidate at top companies
      Interviewer at NVIDIA
      Mentor and coach
    Book structure
      Part 1 - Interview process
      Part 2 - Knowledge questions
      Bonus - Systems design
    Content coverage
      Role types and expectations
      200 plus knowledge questions
      Difficulty levels marked
      30 design scenarios
    What it is not
      Not a textbook
      Not a shortcut

mindmap root((ml-interviews-book)) What it is Free interview prep book Web version available Source on GitHub Author background Candidate at top companies Interviewer at NVIDIA Mentor and coach Book structure Part 1 - Interview process Part 2 - Knowledge questions Bonus - Systems design Content coverage Role types and expectations 200 plus knowledge questions Difficulty levels marked 30 design scenarios What it is not Not a textbook Not a shortcut

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What do people build with it?

USE CASE 1

Work through 200+ ML knowledge questions sorted by difficulty to find gaps before a technical interview

USE CASE 2

Study the 30 open-ended system design questions that nearly every ML company includes in their interviews

USE CASE 3

Understand what interviewers are actually evaluating when they score ML candidates so you can present your knowledge more effectively

USE CASE 4

Use the difficulty level markers to focus prep time on senior-level questions if interviewing for a research or lead role

What is it built with?

HTML

How does it compare?

	chiphuyen/ml-interviews-book	hiddendevj/crawler_illegal_cases_in_china	invertase/rdash-angular
Stars	4,623	4,612	4,650
Language	HTML	HTML	HTML
Setup difficulty	easy	easy	moderate
Complexity	1/5	1/5	2/5
Audience	researcher	developer	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Read the free web version directly, no installation required.

License not described in the explanation.

So what is it?

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.

Copy-paste prompts

Prompt 1

Based on the knowledge questions in ml-interviews-book, quiz me on core machine learning concepts at mid-level difficulty, ask one question at a time and tell me if my answer is correct.

Prompt 2

I have an ML systems design interview at a tech company next week. Using the 30 design questions from ml-interviews-book as a guide, help me practice designing a real-time recommendation system.

Prompt 3

What are the differences between research, applied, and engineering ML roles as described in ml-interviews-book, and which type of interview questions should I focus on for each?

Prompt 4

Based on the evaluation criteria in ml-interviews-book, review my answer to this ML knowledge question and tell me what an interviewer would mark me down for: [paste your answer].

Frequently asked questions