ML & AI Systems
The model is one box on the whiteboard. The interview is about everything around it.
5 lessons · ~52 min
By the end you can
- Turn a vague product goal into one prediction, judged by offline, online, and guardrail metrics
- Catch label leakage, position bias, and training/serving skew before they ship
- Design the retrieve-then-rank funnel and size each stage with numbers
- Ship models safely: latency budgets, fallbacks, A/B tests, and drift monitoring
- Design an LLM assistant with permission-aware retrieval, evaluation, and a token budget
Lessons
- 1What exactly are you predicting10 minHow the ML design round is graded, turning a product goal into one prediction, and the metrics that judge it.
- 2Where the labels come from11 minClicks vs ratings, the leaked feature that tests at 95%, position bias, and training/serving skew.
- 3Find 500, then rank 5010 minWhy one model can't score the catalog, embedding search, new items, and rules that never get a vote.
- 4Shipping without breaking the homepage10 minOffline, nearline, and online work, a 200 ms budget with fallbacks, A/B test sizes, and drift.
- 5A chatbot over your docs, end to end11 minRetrieval with permissions, evaluation you can trust, the token bill, and the interview script for LLM designs.
Design with it after
The design library has full system design prompts on a whiteboard canvas with AI feedback. Every one of them gives you a place to apply this.
Open the design library