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Machine Learning Interviews

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Basic Information

This repository is a practical, curated study guide for preparing technical interviews in Machine Learning and AI engineering roles, with an emphasis on interviews at large technology companies. It was compiled from the author's personal interview preparation notes and experience receiving offers from several major firms. The README organizes the common interview modules encountered in ML roles and indicates which topics to study. The repo is intended for candidates aiming for positions such as Machine Learning Engineer or Applied Scientist and focuses on the breadth of technical preparation rather than company-specific logistics. It highlights updated content as of 2025 and points to related material maintained by the author for deeper or adjacent topics in production deep learning and agentic AI systems.

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https://github.com/alirezadir/Machine-Learning-Interviews
Github Repository
https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/README.md

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Features
The repository is organized into clear chapters covering core interview modules: general algorithms and data structures, ML-specific coding problems, ML fundamentals and breadth, ML system design, behavioral interview guidance, and a pointer to agentic AI systems materials. Each chapter is represented by a markdown document with targeted topics and study pointers. The README includes visual badges indicating license and activity, a cover image, and metadata about recent updates. The project links to complementary repositories maintained by the author for production-level deep learning and agentic AI, and it invites community feedback and pull requests for contributions. The content is structured for iterative study and practical interview readiness.
Use Cases
This guide helps candidates structure their interview preparation by breaking the ML interview process into discrete modules and recommending study focus areas for each module. It clarifies which technical skills are commonly tested, such as algorithms, ML coding, theoretical fundamentals, and system design, and it also addresses behavioral preparation. The author’s experience with successful interviews at major companies offers practical perspective on expectations and prioritization. Supplementary repositories are recommended for hands-on system design and production deep learning practice. The repo is useful as a checklist and roadmap for self-study, targeted practice, and community-driven improvements through contributions and pull requests.

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