Free ML Interview Playbook

A free interview guide for machine-learning-heavy software roles is spreading among computer science students as hiring loops put more weight on applied artificial intelligence work. (x.com) The resource was shared in an X post by the account @_vmlops as an “Applied AI & ML Interview Playbook” in a Google Drive link, with replies and reposts from candidates using it for interview prep. (x.com) The playbook is framed around machine learning engineer and applied artificial intelligence interviews, not just standard LeetCode-style coding screens, and it is aimed at students targeting large technology companies. (x.com) Machine learning interviews test a different layer of knowledge than a classic algorithms round: model training, evaluation, deployment, monitoring, and trade-offs in production systems. OpenAI job listings for machine learning engineers and research engineers describe work that includes deploying models, building distributed machine learning systems, and turning research into products. (openai.com 1) (openai.com 2) Google’s machine learning careers page groups those jobs inside engineering, and OpenAI’s interview guide says applicants should expect a structured hiring process with screening and skills-based evaluation. (careers.google.com) (openai.com) That helps explain why candidates keep looking for prep material that sits between textbook machine learning and software engineering drills. Public interview guides for Google and Meta machine learning engineer roles describe loops that combine coding, machine learning depth, and system design. (igotanoffer.com) (tryexponent.com) Free material also fills a price gap. Huyen Chip’s “Introduction to Machine Learning Interviews” is available online at no cost, but much of the broader interview-prep market is now split across paid coaching, paid platforms, and scattered blog posts. (huyenchip.com) (interviewaibox.co) The interest around this playbook also tracks a shift in the jobs themselves. Recent OpenAI roles list responsibilities such as model deployment, data systems, integrity tooling, forecasting, and applied voice systems, all of which blur the line between software engineer, machine learning engineer, and research engineer. (openai.com 1) (openai.com 2) (openai.com 3) For students, the message in the post is practical: algorithm practice is still part of the loop, but it is no longer the whole loop for many applied artificial intelligence jobs. (x.com)