Is Machine-Learning-Interviews open source?

Yes — alirezadir/Machine-Learning-Interviews is open source, released under the MIT license.

What language is Machine-Learning-Interviews written in?

alirezadir/Machine-Learning-Interviews is primarily written in Jupyter Notebook.

How popular is Machine-Learning-Interviews?

alirezadir/Machine-Learning-Interviews has 8.6k stars on GitHub and is currently cooling off.

Where can I find Machine-Learning-Interviews?

alirezadir/Machine-Learning-Interviews is on GitHub at https://github.com/alirezadir/Machine-Learning-Interviews.

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alirezadir/Machine-Learning-Interviews

A Field Manual for Surviving Big-Tech ML Interviews

A curated survival kit for ML engineering interviews, assembled from the notes that got the author hired at Meta, Google, Amazon, Apple, and Roku.

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What it does

It organizes the author’s personal interview prep into six chapters covering the modules big tech typically throws at ML engineering candidates: coding, ML coding, fundamentals, system design, agentic AI, and behavioral questions. The material is explicitly drawn from the author’s own successful loops at Meta, Google, Amazon, Apple, and Roku, where he landed offers for ML specialist and applied scientist roles. It is not a generic algorithm repository; it is a narrowly focused study guide for an interview track that the author notes lacks the standardized structure of software engineering interviews.

The interesting bit

The guide treats the interview as a system with undocumented inputs. Rather than pretending FAANG ML loops are uniform, it maps idiosyncratic components—ML system design, ML-specific coding, and now agentic AI systems—across different companies under one roof. It is essentially one engineer’s battle-tested notebook scaled into a public curriculum.

Key highlights

Covers six modules: general coding, ML coding, ML fundamentals, ML system design, agentic AI systems, and behavioral interviews
Updated in 2025 with a dedicated chapter linking out to agentic AI systems design and engineering trends
Drawn from real offers at Meta, Google, Amazon, Apple, and Roku for ML Engineer and Applied Scientist positions
Explicitly acknowledges that ML interviews lack a standardized structure, unlike typical SWE loops
Points to a companion repo on production-level deep learning for extra system design context

Caveats

Heavily geared toward ML Engineer and Applied Scientist tracks; Data Science and research scientist interviews follow different structures
Content reflects the author’s personal experience rather than official company rubrics, so your loop may differ
The 2025 agentic AI chapter redirects to a separate repository rather than hosting content inline

Verdict

Worth bookmarking if you are targeting FAANG-style ML engineering roles and need a map of what to study beyond LeetCode. Skip it if you are looking for a research scientist deep-dive or a hands-on coding framework—this is a reading list and study guide, not a library.

Frequently asked

What is alirezadir/Machine-Learning-Interviews?: A curated survival kit for ML engineering interviews, assembled from the notes that got the author hired at Meta, Google, Amazon, Apple, and Roku.
Is Machine-Learning-Interviews open source?: Yes — alirezadir/Machine-Learning-Interviews is open source, released under the MIT license.
What language is Machine-Learning-Interviews written in?: alirezadir/Machine-Learning-Interviews is primarily written in Jupyter Notebook.
How popular is Machine-Learning-Interviews?: alirezadir/Machine-Learning-Interviews has 8.6k stars on GitHub and is currently cooling off.
Where can I find Machine-Learning-Interviews?: alirezadir/Machine-Learning-Interviews is on GitHub at https://github.com/alirezadir/Machine-Learning-Interviews.