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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
| Date | Stars |
|---|---|
| 2026-07-31 | 8650 |
| 2026-08-01 | 8650 |
| 2026-08-06 | 8692 |
Today
+42 stars today
This week
— stars this week
This month
— stars this month
Momentum
168.0
growth rate 0.00%/day
<p align="center"> <img width="720" src="src/imgs/cover.png"> </p> [](LICENSE) [](https://github.com/psf/black) [](https://github.com/alirezadir/Machine-Learning-Interviews/stargazers) [](https://github.com/alirezadir/Machine-Learning-Interviews/network) [](https://github.com/alirezadir/Machine-Learning-Interviews/commits/main) [](https://github.com/alirezadir/Machine-Learning-Interviews/issues) [](https://github.com/alirezadir/Machine-Learning-Interviews/graphs/contributors) [](https://twitter.com/intent/tweet?text=Check%20out%20Machine%20Learning%20Interviews%20by%20%40alirezadira%20%E2%80%94%20A%20guide%20to%20prepare%20for%20ML%20interviews!&url=https%3A%2F%2Fgithub.com%2Falirezadir%2FMachine-Learning-Interviews&hashtags=MachineLearning,MLinterviews,AI) # AI / Machine Learning Interviews :robot: This repo aims to serve as a guide to prepare for **AI and ML Technical interviews** for relevant roles at big tech companies (in particular FAANG). It has compiled based on the author's personal experience and notes from his own interview preparation, when he received 5 simultaneous offers from Meta (ML Specialist), Google (ML Engineer), Amazon (Applied Scientist), Apple (Applied Scientist), and Roku (ML Engineer) in 2020, and repeated offers from Amazon and Apple in 2025 (AI Tech Lead). > **Remember:** Interviewing is a skill and the more skillful you are, the better the results will be. The following components are the most commonly used interview modules for technical ML roles at different companies. We will go through them one by one and share how one can prepare: <center> |Chapter | Content| |---| --- | | Chapter 1 | [General Coding - DSA (Data Structures and Algorithms)](src/lc-coding.md) | | Chapter 2 | [ML Coding](src/MLC/ml-coding.md) | | Chapter 3 | [ML Fundamentals/Breadth (classic ML, LLMs, multimodal AI, and more)](src/ml-fundamental.md)| | Chapter 4 | [ML/GenAI/LLM System Design](src/MLSD/ml-system-design.md)| | Chapter 5 | [Agentic AI Systems](https://github.com/alirezadir/Agentic-AI-Systems.git)| | Chapter 6 | [Behavioral Interviews](src/behavior.md)| | Resources | [GenAI Learning Resources](src/genai-resources.md)| | | | </center> ## News I now offer **1:1 AI/ML interview coaching & mock interviews** for AI/ML Engineers, Applied AI Scientists, Research Engineers, Research Scientists, AI Strategists, Engineering Managers, and senior AI leaders. Topics include technical interviews (AI/ML system design, GenAI & Agentic AI fundamentals, ML fundamentals, AI coding, and more), behavioral interviews, and leadership interviews. Learn more: [https://aimlinterviews.io](https://aimlinterviews.io) --- :newspaper: This repository is now **AIMLInterviews**, updated for 2026 with expanded LLM, multimodal AI, post-training, and GenAI system-design content. **Notes:** * AI and ML interviews at different companies do not follow a unique structure. However, I found the components very similar across FAANG companies. Startup interviews are often tailored to their own use cases and problems at hand, while larger companies tend to follow a more consistent structure. * The guide here is mostly focused on *AI / ML Engineeri
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Read on GitHubAlireza Dirafzoon · AWS · United States
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Anirudh Thatipelli · PhD, University of Central Florida · United States
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Erjan K · Netherlands
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:87f0b010b5e951ab, topic:ai-agents, topic:agentic, readme:agentic
matched fp:87f0b010b5e951ab, topic:deep-learning