This repository serves as a comprehensive guide for preparing for Machine Learning (AI) Engineering technical interviews, particularly at large tech companies like FAANG. It consolidates essential knowledge across coding, ML fundamentals, system design, and behavioral aspects, drawing from the author's successful interview experiences.

How It Works

The guide breaks down interview preparation into six key modules: General Coding (Algorithms and Data Structures), ML Coding, ML Fundamentals/Breadth, ML System Design, Agentic AI Systems, and Behavioral questions. Each module provides curated resources and preparation strategies tailored to the specific requirements of ML-focused roles.

Quick Start & Requirements

• Installation: No installation required; it's a curated collection of markdown resources.
• Prerequisites: Familiarity with core computer science concepts, machine learning principles, and common programming languages (e.g., Python) is assumed.
• Resources:
  • General Coding: src/lc-coding.md
  • ML Coding: src/MLC/ml-coding.md
  • ML Fundamentals: src/ml-fundamental.md
  • ML System Design: src/MLSD/ml-system-design.md
  • Agentic AI Systems: https://github.com/alirezadir/Agentic-AI-Systems.git
  • Behavioral: src/behavior.md

Highlighted Details

• Focuses on common interview modules for ML Engineer and Applied Scientist roles at top tech companies.
• Includes updated content for ML System Design (2023) and Agentic AI Systems (2025).
• Offers insights based on the author's personal interview experiences and offers from Meta, Google, Amazon, Apple, and Roku.
• Welcomes contributions via pull requests.

Maintenance & Community

• The repository is updated, with specific modules noted as updated in 2023 and 2025.
• Contributions are actively encouraged.

Licensing & Compatibility

• The repository itself does not specify a license in the provided README snippet. Users should verify licensing for any code or content derived from this repository.

Limitations & Caveats

The guide primarily targets ML Engineer and Applied Scientist roles, and may be less directly applicable to Data Science or ML Research Scientist roles, though some modules remain relevant. The structure of ML interviews can vary significantly between companies, and this guide offers a generalized approach.