Start by clarifying requirements and constraints, then walk through the end-to-end ML system: data sources, retrieval, ranking, feature infrastructure, serving, training, evaluation, and cold-start/diversity strategies. Emphasize trade-offs and how you would measure success with offline metrics and online A/B tests.
Pro tip: Anchor your design to DoorDash's business goals—e.g., optimizing for order completion and user retention—and explicitly discuss how you'd handle the cold-start problem for new users and restaurants, since this is critical for a marketplace.
Ask about scale (users, items), latency, personalization goals, and business metrics. Define functional and non-functional requirements.
Identify user, item, and interaction data. Describe feature engineering, storage (e.g., feature store), and real-time vs. batch features.
Explain candidate generation (e.g., collaborative filtering, embeddings) and ranking (e.g., learning-to-rank with GBDT/NN). Discuss multi-stage funnel.
Cover model serving (low-latency, scalable), training pipeline (retraining frequency), and evaluation (offline metrics like NDCG, online A/B tests).
Address new user/item cold-start (e.g., content-based, contextual bandits) and diversity (e.g., re-ranking, MMR) to avoid filter bubbles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.