Machine learning interviews are a different beast compared to the more standardized software engineering rounds. They often vary wildly between companies, with no single “LeetCode” equivalent to master. This repo, Machine-Learning-Interviews by alirezadir, compiles a practical, experience-driven preparation guide focused on ML and AI engineering roles at FAANG and similar tech giants.
What Machine-Learning-Interviews offers and its architecture
The repository is a collection of Jupyter notebooks, organized into six core modules that reflect the broad spectrum of topics encountered in ML interviews at major companies like Meta, Google, Amazon, Apple, and Roku. These modules are:
- General coding algorithms (the basics still matter)
- Machine learning coding exercises
- Breadth questions on ML fundamentals
- ML system design
- Agentic AI systems (a new dimension added in 2025, reflecting evolving interview focus)
- Behavioral interview preparation
The notebooks are not just random questions but curated based on the author’s own successful interview experiences. This gives them a realistic edge, showing what questions actually matter and how to approach them.
Under the hood, the repo uses Python in Jupyter notebooks, which is a natural fit for ML practitioners. The notebooks mix coding challenges with conceptual questions and design discussions. There’s also a link to a supplementary repository focused on production-level deep learning system design, which complements the interview prep by giving insights into real-world production challenges.
This structure is particularly useful given the lack of standardization in ML interviews. Unlike typical SWE interviews, which often follow a known pattern, ML interviews can differ widely in focus and depth. This repo attempts to organize that chaos into a coherent learning path.
Why this repo stands out for ML interview preparation
What distinguishes this compilation is its grounding in real interview experiences at top-tier companies. Many ML interview resources are scattered or theoretical, but this one blends coding, theory, system design, and behavioral preparation in one place.
The inclusion of an “Agentic AI systems” module is notable and timely. As AI roles expand beyond classic ML models into autonomous agent architectures and multi-agent systems, interviewers are starting to probe candidates on these concepts. This repo anticipates that shift by adding relevant material for 2025 and beyond.
The code quality matches typical Jupyter notebook style — readable, annotated, and designed for interactive exploration rather than production deployment. This means it’s easy to run and modify exercises but not a plug-and-play tool.
The tradeoff here is the repo’s reliance on self-driven study. It’s comprehensive but requires discipline to follow through. There’s no automated testing or grading framework, so users must be proactive in practicing and understanding.
Explore the project
The repo is mainly notebooks organized in folders by topic. The README provides an overview and links to each module:
general_coding_algorithms/ml_coding_exercises/ml_fundamentals_breadth/ml_system_design/agentic_ai_systems/behavioral_interviews/
Each folder contains multiple .ipynb files that you can open with Jupyter or compatible notebook viewers.
For example, the ML system design notebooks walk you through key concepts like feature pipelines, model deployment, monitoring, and tradeoffs in production systems. The agentic AI notebooks introduce concepts around autonomous agents and multi-agent problem solving, which are becoming increasingly relevant.
Here’s a peek at a typical notebook snippet outlining a question and solution approach:
# Example: ML coding exercise
# Implement gradient descent for a linear regression model
def gradient_descent(X, y, lr=0.01, epochs=100):
m, n = X.shape
theta = np.zeros(n)
for _ in range(epochs):
predictions = X.dot(theta)
errors = predictions - y
gradient = X.T.dot(errors) / m
theta -= lr * gradient
return theta
This style makes it easy to adapt exercises, add notes, or test alternative approaches interactively.
Verdict
Machine-Learning-Interviews is a solid, experience-backed resource for anyone preparing for ML engineering roles at major tech companies. It’s especially useful if you want a structured, multi-dimensional prep path that goes beyond just coding challenges to include system design and behavioral topics.
The addition of agentic AI systems shows foresight into the evolving interview landscape, making it relevant for candidates preparing for 2025 and beyond.
That said, it’s not a turnkey solution — it demands self-motivation and doesn’t replace hands-on coding practice or real interview experience. But as a curated, practical guide that organizes a fragmented interview space, it’s worth your time if you’re serious about landing a role at a FAANG or similar company.
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→ GitHub Repo: alirezadir/Machine-Learning-Interviews ⭐ 8,139 · Jupyter Notebook