khangich/machine-learning-interview

This repository is a study guide created by a software and machine learning engineer with 10 years of experience who received job offers from companies like Google, LinkedIn, Snapchat, Coupang, and StitchFix. The guide is aimed at people preparing for machine learning engineering interviews, particularly at large technology companies. The author also wrote a book on machine learning system design, which is referenced throughout the README. The guide covers several main areas: coding exercises (specifically programming challenges on LeetCode organized by category), SQL practice, core programming language concepts for Python and Java, statistics and probability questions, and big data topics. Not all companies ask every type of question, and the author is clear that LeetCode problems are not required by every employer. The guide links out to external resources like spreadsheets, blog posts, and courses rather than containing all the material itself. A section called Machine Learning Design walks through real-world design problems, such as building a YouTube recommendation system, predicting ad clicks, estimating delivery times, and ranking search results for Airbnb. These are the kinds of open-ended design questions interviewers at large companies use to see how a candidate thinks about building AI products at scale. The repository also links to a file listing how top companies actually apply machine learning in their products, and an advanced topics file for deeper study. There are pointers to the author's blog for interview stories, a one-week pre-interview checklist, and a quiz for testing machine learning knowledge. The guide is a curated roadmap rather than a self-contained course. It tells you what to study and where to go, rather than teaching each topic from scratch. If you are preparing for a machine learning engineering role at a major tech company and want a structured list of topics to cover, this is a starting point.