Disclaimer: Study materials and personal frameworks mentioned are for informational purposes based on common industry practices and popular study techniques. If you are preparing for these interviews, I can help you:
For a more comprehensive guide, you can refer to Ali Aminian's PDF portable guide on machine learning system design interviews. This guide provides an in-depth overview of the key concepts, system design considerations, and tips for acing the interview.
Ali Aminian is a senior machine learning engineer and interview coach who has worked at companies like Uber and Meta. Over the years, he distilled his experience into a repeatable methodology for solving any ML system design problem—from “Design YouTube’s Recommendation Engine” to “Build a Fraud Detection Pipeline.”
Never pitch a solution as perfect. Explain why you chose a specific trade-off (e.g., choosing a simpler model to satisfy a strict 20ms latency constraint over a heavy transformer).
What is the target latency for inference (e.g., under 50ms)? What are the computational storage and budget limitations? Ali Aminian is a senior machine learning engineer
As Aminian himself says in many of his talks: “You don’t design ML systems in an interview like you’re building Google Brain. You design them to show how you think. And great thinking fits on a single page—if you know what to leave out.”
What is your ? (e.g., Mid-level, Senior, or Staff Engineer)
Choose appropriate offline (Precision, Recall, ROC-AUC) and online (A/B testing, CTR) metrics.
By internalizing the and practicing with the 10 real-world case studies , you will gain the confidence to tackle any open-ended system design problem. The availability of portable PDF and ebook editions means your preparation can happen anywhere, turning every commute or break into a valuable study session. What is the target latency for inference (e
: Differentiate between explicit feedback (user ratings, likes) and implicit feedback (clicks, dwell time, skips).
Identify user profiles, historical interaction logs, context (device, time of day), and item metadata.
: Don't ramble. Use the 4-step framework as visual anchors on the whiteboard.
| Decision | Option A | Option B | Aminian’s Rule | |----------|----------|----------|----------------| | Serving | Online (real-time) | Batch (hourly) | If latency < 50 ms → online | | Labels | Weak supervision | Human annotated | Start weak, iterate | | Features | Raw text | Embeddings | Embeddings when cross-features matter | Often described as an insider's guide
: Establish both offline metrics (AUC, ROC, MAP@K) and online metrics (Revenue, CTR, Session Duration). 2. Data Engineering and Feature Pipeline
This practical knowledge is captured in his seminal work, co-authored with Alex Xu. Often described as an insider's guide , this book has been recognized for its immense value, reaching the #1 spot in its Amazon category and remaining on the bestseller list for over 20 months, with translations available in multiple languages. It has earned praise from industry professionals, including a Google data scientist who called it "an essential resource" and a Block ML engineer who lauded it for providing "highly relevant, in-depth insights".
Which are you designing? (e.g., Search Ranking, Fraud Detection, Self-Driving Perception)