alirezadir/Machine-Learning-Interviews: A FAANG ML Interview Prep Guide

alirezadir/Machine-Learning-Interviews: A FAANG ML Interview Prep Guide

Machine learning engineering interviews at major tech companies lack the standardized structure of software engineering loops. Candidates face unpredictable modules—ML coding, system design, fundamentals breadth, and behavioral rounds—often without clear preparation frameworks. This fragmentation creates real friction: engineers with strong production experience stumble on theoretical depth, while researchers struggle to demonstrate scalable system thinking. alirezadir/Machine-Learning-Interviews addresses this gap directly. With 8,574 GitHub stars and active maintenance through June 2026, this MIT-licensed repository compiles battle-tested preparation materials from an author who received offers from Meta, Google, Amazon, Apple, and Roku. Whether you're targeting MLE, Applied Scientist, or research engineering roles, this guide provides structured coverage of the interview components that actually appear in big-tech loops.

What is alirezadir/Machine-Learning-Interviews?

alirezadir/Machine-Learning-Interviews is an open-source interview preparation repository maintained by Alireza Dirafzoon, an ML practitioner with direct experience navigating FAANG interview processes. The project falls under the broader category of [INTERNAL_LINK: developer-education-resources] and serves as a curated knowledge base rather than a traditional software tool or library.

The repository's primary language is Jupyter Notebook, reflecting its emphasis on executable explanations and interactive learning materials. Under the MIT License, it permits free use, modification, and distribution—consistent with its stated goal of democratizing access to high-quality interview preparation.

What distinguishes this guide from generic interview advice is its grounding in actual outcomes. The author explicitly notes receiving offers from five major technology companies, lending credibility to the structure and emphasis of each chapter. The repository has attracted 1,501 forks, indicating active community engagement beyond passive star-giving.

The 2026 update significantly expands relevance: Chapters 3 and 4 now cover GenAI/LLM topics including KV cache, GQA (Grouped Query Attention), RoPE (Rotary Position Embedding), MoE (Mixture of Experts), post-training algorithms (SFT, DPO, GRPO, RLVR), PEFT methods, inference optimization, multimodal AI (VLMs, VLAs), and GenAI system design patterns (RAG, agents, guardrails, evaluation). This evolution reflects the shifting landscape of ML engineering roles, where LLM and agentic system expertise increasingly separates qualified candidates from competitive ones.

The author also maintains a dedicated Agentic AI Systems repository for deeper exploration of agentic architectures, and offers 1:1 coaching services through aimlinterviews.io for personalized preparation.

Key Features

Six-Chapter Structured Coverage — The repository organizes preparation into discrete modules matching actual interview structures: General Coding (algorithms and data structures), ML Coding, ML Fundamentals/Breadth, ML System Design, Agentic AI Systems, and Behavioral interviews. This modularity lets candidates target weak areas without redundant review.

2026 GenAI/LLM Expansion — The updated Chapters 3 and 4 address the most significant shift in ML engineering interviews since 2023: deep technical evaluation of large language model internals and systems. Coverage includes foundation model architectures, post-training alignment techniques, parameter-efficient fine-tuning, and production deployment patterns for generative AI.

Practical System Design Focus — Chapter 4 specifically addresses ML system design with updated GenAI/LLM scenarios, bridging the gap between theoretical knowledge and architectural decision-making under interview constraints. This targets a common failure mode where candidates understand models but cannot design scalable serving infrastructure.

Behavioral Preparation Module — Unlike purely technical resources, Chapter 6 acknowledges that senior ML roles require structured behavioral evaluation. The inclusion reflects the author's experience with leadership-track interviews at the companies listed.

Supplementary Production Resources — The repository cross-references the author's Production Level Deep Learning repository, connecting interview preparation to real-world system implementation.

Active Maintenance and Community — With last commit dated June 20, 2026, the repository demonstrates sustained relevance. The author actively solicits contributions via pull requests and maintains issue tracking for community feedback.

Use Cases

Preparing for FAANG ML Engineering Interviews — The primary and explicitly stated use case. Candidates targeting Meta ML Specialist, Google ML Engineer, Amazon Applied Scientist, Apple Applied Scientist, or similar roles can use the chapter structure to systematically cover evaluated competencies. The author's direct experience with these specific loops provides credible prioritization of topics.

Transitioning from Research to Applied ML Roles — Researchers with strong theoretical foundations often lack exposure to production system design and ML-specific coding patterns. The ML Coding and ML System Design chapters address this gap with practical preparation materials.

Upskilling for GenAI/LLM Role Requirements — The 2026 updates serve engineers needing to demonstrate competence in modern generative AI systems. This includes understanding KV cache memory optimization, selecting between post-training algorithms, or designing RAG architectures—capabilities increasingly required even for non-research positions.

Interview Loop Structure Research — Hiring managers and recruiters can reference this guide to understand current industry standards for ML engineering evaluation, particularly the distinction between MLE and Applied Scientist role expectations noted in the repository's caveats.

Behavioral Interview Preparation for Technical Leaders — Senior candidates interviewing for staff+ positions or engineering management tracks can leverage Chapter 6 alongside the technical modules, reflecting the multifaceted evaluation at senior levels.

Installation & Setup

As a knowledge repository rather than installable software, alirezadir/Machine-Learning-Interviews requires only standard Git operations and a compatible document viewer for Jupyter Notebooks.

Clone the repository:

# Clone via HTTPS
git clone https://github.com/alirezadir/Machine-Learning-Interviews.git

# Or clone via SSH
git clone git@github.com:alirezadir/Machine-Learning-Interviews.git

Navigate to the repository structure:

cd Machine-Learning-Interviews
ls -la

The repository organizes content into a src/ directory with subdirectories for each chapter:

• src/lc-coding.md — General coding preparation
• src/MLC/ml-coding.md — ML-specific coding problems
• src/ml-fundamental.md — ML fundamentals and breadth
• src/MLSD/ml-system-design.md — ML system design cases
• src/behavior.md — Behavioral interview preparation

For interactive execution of Jupyter Notebook materials, ensure you have a compatible environment:

# Verify Jupyter installation
jupyter --version

# If needed, install via pip
pip install jupyter

# Launch notebook server from repository root
jupyter notebook

No additional dependencies, build steps, or configuration files are documented in the README. The repository appears designed for direct reading and manual practice rather than automated execution.

Real Code Examples

The README does not contain executable code snippets within the provided content. Instead, it structures reference materials as linked Markdown^{↗ Smart Converter} documents and Jupyter Notebooks. This reflects the repository's nature as a curated guide rather than a code library. Below are the documented structural references that candidates use for practice:

General Coding Reference Path:

<!-- Linked from README -->
[General Coding (Algos and Data Structures)](src/lc-coding.md)

This link points to LeetCode-style preparation materials. Candidates are expected to work through algorithmic problems using their preferred language, with the repository providing problem curation and solution approaches rather than runnable templates.

ML Coding Reference Path:

<!-- Linked from README -->
[ML Coding](src/MLC/ml-coding.md)

The ML Coding module focuses on implementing ML algorithms from scratch and applying libraries like PyTorch or TensorFlow to interview problems. The Jupyter Notebook format suggests executable examples exist in the linked document, though the README itself provides only the entry point.

ML System Design Reference Path:

<!-- Linked from README -->
[ML System Design (Updated for 2026: GenAI/LLM system design)](src/MLSD/ml-system-design.md)

This document contains architectural case studies and design patterns. The 2026 update specifically includes GenAI system design patterns—candidates should expect to diagram and discuss RAG pipelines, agent orchestration, and guardrail implementations.

The repository's Jupyter Notebook primary language indicates that substantial executable content exists in the linked subdocuments, even if the top-level README functions as an organizational hub. Users should explore the src/ directory structure directly for hands-on materials.

Advanced Usage & Best Practices

Module-Specific Deep Dives — The repository explicitly notes that ML interviews lack standardized structure across companies. Effective preparation requires mapping the six chapters to your target role's known loop components. MLE roles typically emphasize ML Coding and System Design; Applied Scientist roles may weight Fundamentals and research-depth questioning more heavily.

Cross-Reference Production Systems Knowledge — The author recommends pairing this guide with the Production Level Deep Learning repository. Interviewers increasingly evaluate whether candidates can bridge prototype models to reliable production systems—this combination addresses both interview performance and actual job competence.

Prioritize 2026 GenAI Content Strategically — Not all roles require deep LLM expertise. Evaluate job descriptions for mentions of generative AI, foundation models, or agentic systems before allocating disproportionate time to KV cache mechanics or RLVR alignment. The repository's breadth allows targeted preparation.

Behavioral Preparation as Differentiator — At senior levels, technical bar-clearing becomes table stakes. Chapter 6's behavioral focus—combined with the author's coaching service—suggests that structured storytelling about ML project ownership and cross-functional collaboration separates offer recipients from rejections.

Contribute Back for Retention — The author solicits pull requests. Active contributors likely reinforce their own learning through teaching, a documented effective study technique for technical retention.

Comparison with Alternatives

The key trade-off is depth versus interview specificity. alirezadir's guide sacrifices textbook comprehensiveness for actionable, loop-matched structure—valuable when preparation time is constrained and target companies are known.

FAQ

Is this repository free to use? Yes, under the MIT License. You can use, modify, and distribute the content with attribution.

Does it cover software engineering interviews or only ML-specific ones? Chapter 1 addresses general coding (algorithms and data structures), but the overall focus is ML engineering roles. Pure SWE candidates may find better-targeted resources.

How current is the GenAI/LLM content? Updated for 2026 as of the last commit on June 20, 2026. This includes KV cache, GQA, RoPE, MoE, SFT, DPO, GRPO, RLVR, PEFT, and agentic system design.

What roles specifically does this prepare for? Machine Learning Engineer, Applied Scientist, Research Engineer, and Research Scientist at big tech companies. Data Science roles have different interview structures and may find partial utility.

Is there executable code or only reading materials? The primary language is Jupyter Notebook, suggesting executable content in linked subdocuments. The top-level README serves as an organizational index.

How can I contribute? Submit a pull request with suggested changes. The author explicitly welcomes feedback and contributions.

Does the author offer personalized help? Yes, 1:1 coaching is available at aimlinterviews.io for ML/AI system design, LLMs, technical, behavioral, and leadership interviews.

Conclusion

alirezadir/Machine-Learning-Interviews fills a genuine preparation gap for ML engineering candidates facing non-standardized big-tech interview loops. Its credibility stems from documented author outcomes—offers from Meta, Google, Amazon, Apple, and Roku—and its evolution to include 2026 GenAI/LLM competencies reflects market demands. The 8,574 stars and 1,501 forks indicate community validation beyond the author's network.

This resource best serves candidates with defined target roles at companies using similar evaluation modules. It is less suited to those seeking comprehensive ML education or pure software engineering preparation. The modular structure enables efficient, targeted study, while the MIT license removes friction from adoption.

For engineers preparing ML interviews in the current market—where LLM system design and agentic AI increasingly appear in loops—this repository provides timely, experience-grounded guidance. Explore the full materials at https://github.com/alirezadir/Machine-Learning-Interviews and consider how each chapter maps to your upcoming interview stages.