{"data":{"slug":"alirezadir-machine-learning-interviews","name":"Machine-Learning-Interviews","tagline":"Guide for Machine Learning/AI technical interviews","github_url":"https://github.com/alirezadir/Machine-Learning-Interviews","owner":"alirezadir","repo":"Machine-Learning-Interviews","owner_avatar_url":"https://avatars.githubusercontent.com/u/5262877?v=4","primary_language":"Jupyter Notebook","stars":8638,"forks":1508,"topics":["agentic","ai","ai-agents","ai-engineering","deep-learning","interview","interview-practice","interview-preparation","interviews","machine-learning","machine-learning-algorithms","scalable-applications","system-design"],"archived":false,"github_pushed_at":"2026-06-20T06:39:46+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/alirezadir-machine-learning-interviews","markdown_url":"https://www.graphcanon.com/tools/alirezadir-machine-learning-interviews.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/alirezadir-machine-learning-interviews","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=alirezadir-machine-learning-interviews","description":"This repo is meant to serve as a guide for Machine Learning/AI technical interviews. ","homepage_url":null,"license":"MIT","open_issues":11,"watchers":95,"ai_summary":"Repository aimed at preparing candidates for ML/AI engineering interviews with insights and topics relevant to roles at major tech companies.","readme_excerpt":"

\n\n

\n\n \n# AI / Machine Learning Interviews :robot: \n\n## News\n\n:newspaper: I now offer 1:1 AI/ML interview coaching for AI/ML Engineers, Applied AI Engineers & Scientists, Research Engineers, Research Scientists, AI Strategists, Engineering Managers, and senior AI leaders.\n\nTopics include ML/AI system design, LLMs & Agentic AI, technical interviews, behavioral interviews, and leadership interviews.\n\nLearn more: https://aimlinterviews.io\n\n---\n\n:newspaper: **News: Updated for 2026**: Chapters 3 and 4 now cover the latest **GenAI / LLM** interview topics — foundation models & LLM internals (KV cache, GQA, RoPE, MoE), **post-training algorithms** (SFT, DPO, GRPO, RLVR, …), **PEFT & inference optimization**, **multimodal AI** (VLMs, **VLAs**, diffusion vs autoregressive), and **GenAI system design** (RAG, agents, guardrails, eval). For deeper agentic content, see the dedicated [Agentic AI Systems](https://github.com/alirezadir/Agentic-AI-Systems.git) repo, with resources, system design summaries, and hands-on coding examples and projects. \n\n---\n\nThis repo aims to serve as a guide to prepare for **Machine Learning (AI) Engineering** 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 offers from Meta (ML Specialist), Google (ML Engineer), Amazon (Applied Scientist), Apple (Applied Scientist), and Roku (ML Engineer).\n\nThe 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:\n\n
\n\n |Chapter | Content|\n |---| --- |\n | Chapter 1 \t| [General Coding (Algos and Data Structures)](src/lc-coding.md)\t | \n| Chapter 2 \t| [ML Coding](src/MLC/ml-coding.md) \t| \t\n| Chapter 3\t| [ML Fundamentals/Breadth (Updated for 2026: LLMs, multimodal AI)](src/ml-fundamental.md)| \n| Chapter 4 \t| [ML System Design (Updated for 2026: GenAI/LLM system design)](src/MLSD/ml-system-design.md)|\n| Chapter 5 \t| [*Agentic AI Systems (2026)*](https://github.com/alirezadir/Agentic-AI-Systems.git)|\n| Chapter 6 \t| [Behavioral](src/behavior.md)| \n| \t| \t| \n\n
\n\n**Notes:**\n* At the time I'm putting these notes together, machine learning interviews at different companies do not follow a unique structure unlike software engineering interviews. However, I found some of the components very similar to each other, although under different naming.\n\n* The guide here is mostly focused on *Machine Learning Engineer* (and Applied Scientist) roles at big companies. Although relevant roles such as \"Data Science\" or \"ML research scientist\" have different structures in interviews, some of the modules reviewed here can be still useful. \n\n\n* As a supplementary resource, you can also refer to my [Production Level Deep Learning](https://github.com/alirezadir/Production-Level-Deep-Learning) repo for further insights on how to design deep learning systems for production. \n\n\n\n# Contribution\n* Feedback and contribution are very welcome :blush: \n**If you'd like to contribute**, please make a pull request with your suggested changes).","github_created_at":"2021-01-31T03:52:09+00:00","created_at":"2026-07-07T17:41:27.713443+00:00","updated_at":"2026-07-28T18:00:17.53647+00:00","categories":[{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"agentic-ai","name":"agentic-ai"},{"slug":"llms","name":"llms"},{"slug":"machine-learning-algorithms","name":"machine-learning-algorithms"},{"slug":"ml-interview-guide","name":"ml interview guide"},{"slug":"system-design","name":"system design"}],"trust":{"provenance":{"is_fork":false,"github_id":334567245,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-28T18:00:16.592Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":0,"days_since_push":38,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-09T11:35:57.240Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-28T18:00:17.247Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-07-28T18:00:17.247Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-28T18:00:17.247Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"The repository itself is free under the MIT license but offers supplementary 1:1 AI/ML coaching services at an additional cost, which is outlined on https://aimlinterviews.io"},"requirements":{"notes":["- Python and Jupyter Notebook knowledge for interacting with the material.","- Basic to advanced understanding of ML concepts to grasp the depth covered in the repository."]},"constraints":{"pricing_model":"freemium"},"when_to_use":["- You are targeting roles such as Machine Learning Engineer or Applied Scientist positions at major tech companies like FAANG.","- You need to prepare for updates in ML/AI interviews covering advanced topics, including LLMs and multimodal AI.","- You require specific preparation for GenAI system design with chapters dedicated to RAG, agents, guardrails, and evaluation.","- You are interested in insights that align with the experiences of someone who has been through the interview process at FAANG companies."],"when_not_to_use":["- If your focus is on roles such as Data Science or ML research scientist where the structure of interviews differs significantly from Machine Learning Engineer positions.","- For candidates who do not aim to work at big tech companies but rather in startups or smaller organizations, as this tool emphasizes preparation for FAANG and similar entities.","- If you are looking for general developer tools that cover a wide range of programming languages and frameworks beyond ML-related content."],"source":"enrich:decision_facts","observed_at":"2026-07-09T08:03:03.056Z"},"constraint_facets":{"pricing_model":"freemium"},"decision_summary":[{"label":"Pricing","value":"freemium - The repository itself is free under the MIT license but offers supplementary 1:1 AI/ML coaching services at an additional cost, which is outlined on https://aimlinterviews.io"},{"label":"Requirements","value":"- Python and Jupyter Notebook knowledge for interacting with the material.; - Basic to advanced understanding of ML concepts to grasp the depth covered in the repository."},{"label":"Adopt for","value":"Machine-Learning-Interviews is aimed at candidates preparing for technical ML/AI interviews, focusing on deep topics including LLM internals and GenAI system design. Here are critical facts for decision making about its适"}]}}