Most ML engineers fail system design interviews — not because they lack knowledge, but because they lack structure.

After sitting on both sides of the interview table, the pattern is clear: brilliant engineers jump straight into matrix factorization and deep learning before even understanding what they're building.

That's why I wrote a 2-part series on cracking ML system design interviews.

Part 1 — The Framework A structured approach to any ML design question, starting with requirements gathering:

• Scope clarification (region, goals, query types)

• Writing down constraints on the whiteboard (yes, even on Zoom)

• Communicating your thinking systematically under pressure

Part 2 — Netflix Case Study A real-world walkthrough of designing a recommendation system — the kind of problem you'll actually face in interviews AND on the job. This part applies the framework with concrete, production-level implementation details.

If you're preparing for ML roles at top tech companies, this series might be worth your time.

🔗 Part 1: buff.ly/2d5U0Q6

🔗 Part 2: buff.ly/NzFQrJ0

What's the hardest ML system design question you've faced? Drop it in the comments 👇