aimlinterviews

This repository serves as a practical guide for preparing for AI and Machine Learning technical interviews, particularly for roles at large tech companies like FAANG. It is compiled from the author's personal experience, including receiving multiple offers from Meta, Google, Amazon, Apple, and Roku. The guide is structured into several chapters, each focusing on a key interview component: - **Chapter 1: General Coding (DSA)** – Covers data structures and algorithms, a common first-round technical screen. - **Chapter 2: ML Coding** – Focuses on implementing machine learning algorithms from scratch, often in a coding environment. - **Chapter 3: ML Fundamentals** – Reviews core ML concepts, including classic machine learning, large language models (LLMs), and multimodal AI, to test breadth of knowledge. - **Chapter 4: ML/GenAI/LLM System Design** – Addresses designing scalable ML systems, including generative AI and LLM-based architectures. - **Chapter 5: Agentic AI Systems** – Links to a separate repository on building agentic AI systems, a growing area in interviews. - **Chapter 6: Behavioral Interviews** – Provides guidance and a worksheet template for behavioral and leadership questions. - **Resources** – Lists learning materials for generative AI. - **AI Tutor** – Offers an MCP server (`aimlinterviews-mcp`) that can turn any MCP-compatible AI assistant into an interview coach, providing curriculum problems, hints, and answer reviews. The content is updated for 2026, with expanded sections on LLMs, multimodal AI, post-training, and GenAI system design. It is primarily aimed at AI/ML Engineering, Applied Science, and Tech Lead roles, though some modules may be useful for data science or research scientist positions. The author notes that interview structures vary by company, but the components are similar across FAANG, while startups often tailor interviews to their specific use cases. Contributions are welcome via pull requests. The repository is available in English, Simplified Chinese, and Persian.