Start by clarifying the product context (e.g., Facebook Places, Instagram location tags) and scale requirements, then walk through the ML pipeline end-to-end: data acquisition, feature engineering, candidate generation, ranking, and serving. Emphasize trade-offs at each stage, such as latency vs. relevance, and how you would measure success with online metrics and A/B tests.
Pro tip: Anchor your design around Meta's existing infrastructure (e.g., PyTorch, FBLearner, Presto) and highlight how you'd handle cold-start and real-time updates, since interviewers value practical deployment experience over theoretical perfection.
Ask questions to understand the product surface (e.g., check-ins, recommendations), scale (DAU, QPS), latency constraints, and success metrics. Define the problem precisely before diving into design.
Describe data sources (user check-ins, reviews, social graph, geospatial data) and how to build features (user embeddings, place embeddings, contextual features). Discuss data pipelines and storage.
Explain a two-stage approach: candidate generation (e.g., collaborative filtering, geo-based retrieval, ANN) to narrow millions to hundreds, then ranking (e.g., GBDT, DNN) to order them. Mention handling cold-start and diversity.
Outline the serving architecture: precomputation, caching, real-time inference, and fallbacks. Discuss how to scale with sharding, load balancing, and monitoring for latency and quality.
Define offline metrics (recall@k, NDCG) and online metrics (CTR, engagement). Describe A/B testing methodology, guardrail metrics, and how to iterate based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.