📰 Your favorite Machine Learning Interviews repo is now AIMLInterviews 🤖
📰 I now offer limited 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. Topics include ML/AI system design, LLMs & Agentic AI, technical interviews, behavioral interviews, and leadership interviews. Learn more at: https://aimlinterviews.io
This 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).
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:
| Chapter | Content |
|---|---|
| Chapter 1 | General Coding - DSA (Data Structures and Algorithms) |
| Chapter 2 | ML Coding |
| Chapter 3 | ML Fundamentals/Breadth (Updated for 2026: LLMs, multimodal AI) |
| Chapter 4 | ML/GenAI/LLM System Design |
| Chapter 5 | Agentic AI Systems |
| Chapter 6 | Behavioral Interviews |
Notes:
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AI / ML interviews at different companies do not follow a unique structure unlike SWE interviews. However, I found some of the components very similar to each other, although under different naming.
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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.
📰 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 repo, with resources, system design summaries, and hands-on coding examples and projects.
- Feedback and contribution are very welcome 😊 If you'd like to contribute, please make a pull request with your suggested changes).