📰 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.
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 (Algos and Data Structures) |
| Chapter 2 | ML Coding |
| Chapter 3 | ML Fundamentals/Breadth (Updated for 2026: LLMs, multimodal AI) |
| Chapter 4 | ML System Design (Updated for 2026: GenAI/LLM system design) |
| Chapter 5 | Agentic AI Systems (2025) |
| Chapter 6 | Behavioral |
Notes:
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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.
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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.
- As a supplementary resource, you can also refer to my Production Level Deep Learning repo for further insights on how to design deep learning systems for production.
- Feedback and contribution are very welcome 😊 If you'd like to contribute, please make a pull request with your suggested changes).