How to Prepare for AI and ML Interviews at Big Tech Without Chaos in Your Head
Preparing for AI interviews usually turns into chaos. You need classic LeetCode, writing neural networks from scratch in pure PyTorch, and system design. On top of that, interviewers constantly ask about fresh stuff like LLMs, multimodal models, or agents.
The repository AIMLInterviews was born as developer Alireza Dir's personal cheat sheet. Using this systematic approach, in 2020 he received five offers simultaneously — from Meta, Google, Amazon, Apple, and Roku. In 2025 he repeated the result, landing AI Tech Lead positions at Amazon and Apple.
What's Inside the Guide
The entire base is broken down into six main chapters plus a separate section with links to GenAI materials. The author updated the content based on 2026 trends, focusing on Agentic AI, model post-training, and LLM architecture.
The structure looks like this:
- General Coding (DSA) — algorithms and data structures.
- ML Coding — writing ML code from scratch.
- ML Fundamentals — theory from classical ML to transformers.
- ML/GenAI System Design — designing architectures and systems.
- Agentic AI Systems — autonomous agents and tool calling.
- Behavioral Interviews — preparing for behavioral sections.
Coding and ML Theory
The coding section is divided into two parts. Regular DSA tests classic problem-solving with LeetCode-style tasks. In the ML coding section, candidates are expected to implement basic neural network components in Python without external frameworks. You should be able to write an attention mechanism, custom loss function, or a simple layer from scratch.
In the theory block, the author collected foundational concepts: from optimizer mathematics and regularization to attention in transformers and multimodal architectures.
System Design and AI Agents
System design often becomes the most challenging stage for mid-level and senior engineers. The ML System Design section shows how to design scalable AI services. The updated materials cover working with context windows, data pipelines for LLMs, and ways to reduce inference latency.
A separate chapter on Agentic AI reflects current reality. Engineers are increasingly required to understand how to build autonomous agents, organize external function calls, and manage planning logic.
Behavioral Stage
At a large corporation, technical hard skills won't save you if you fail the behavioral section. The author touched on this as well. Chapter six contains a framework for preparing answers using the STAR method with examples of questions commonly asked at FAANG.
Who Will Benefit from This Repository
The project is useful for AI/ML engineers, Applied Scientists, and Tech Leads planning interviews at international companies or major domestic corporations.
It's worth noting that the repository is structured more as a structured knowledge map than as a comprehensive textbook. Some topics are presented as links to third-party articles or brief author notes. Nevertheless, as a preparation plan or checklist for finding gaps in your knowledge, this guide works great.
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