The depth they wanted here was kind of brutal given the time crunch.
Start by clarifying the product context and requirements (e.g., event types, user scale, latency, cold-start). Then outline a high-level system architecture covering data, features, models, and serving, and dive into a specific model design (e.g., two-tower retrieval + ranking) with implementation details and trade-offs.
Pro tip: Emphasize how you would evaluate the system offline and online, and how you'd handle cold-start and feedback loops—these are critical at Meta's scale and show you think beyond model accuracy.
Ask questions to understand the product: what are events (e.g., local, online, social), user scale, latency constraints, and business goals. Define success metrics (e.g., CTR, engagement, diversity).
Describe the end-to-end pipeline: data collection, feature engineering, training, serving, and feedback loop. Mention components like candidate generation, ranking, and re-ranking.
Propose a two-stage approach: a two-tower retrieval model for candidate generation and a deep ranking model (e.g., DLRM or Wide & Deep) for scoring. Explain embeddings, feature inputs, and loss functions.
Cover training infrastructure (e.g., distributed training), serving latency, model updates, and trade-offs between relevance, diversity, and freshness. Mention A/B testing and online evaluation.
Talk about cold-start (new users/events), popularity bias, and feedback loops. Propose solutions like content-based features, exploration, and debiasing techniques.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.