Yelp·Data Scientist·Onsite - System Design / Architecture
Jul 2026
Yelp DS interview that was basically a full ML systems design gauntlet compressed into one session. They wanted end-to-end depth on a recommender system for a cold-start-heavy content app, and I mean real depth, not just buzzword soup.
- Design a two-stage recommendation system (candidate generation plus ranking) for a content app with sparse user interactions and frequent new items. Walk through your model choices, loss functions, and negative sampling strategy for each stage.
- How would you handle user and item cold-start in both stages of the recommendation pipeline? What specific features would you rely on?
- How do you balance exploration and exploitation in this recommendation system? Which specific strategies would you use?
- What offline metrics and online KPIs would you define for this recommender, and how would you design an A/B test that controls for selection bias using inverse propensity scoring or doubly robust estimators?
- How would you meet strict latency requirements (p50 under 50ms and p99 under 200ms end-to-end) for this recommendation system at inference time?
“I led with a two-tower retrieval model for candidate gen because it handles cold-start reasonably well if you lean on item content features instead of pure ID embeddings.”