This is a classic ML system design prompt but the scope creep is real.
Start by clarifying the product context and objectives, then outline a high-level architecture covering data, features, models, and serving. Dive into key components like candidate generation, ranking models, and evaluation metrics, emphasizing trade-offs and scalability.
Pro tip: Anchor your design around Meta's core metrics like meaningful social interactions and long-term user satisfaction, and explicitly discuss how you'd handle cold-start and fairness to show product and ethical maturity.
Ask questions to understand the product (e.g., short-form videos like Reels), scale, and business objectives. Define success metrics such as watch time, engagement, and user satisfaction.
Sketch the end-to-end system: data ingestion, feature store, training pipeline, model serving, and feedback loop. Mention offline vs. online components.
Explain candidate generation (e.g., collaborative filtering, embeddings), ranking (e.g., deep learning models with features), and re-ranking (e.g., diversity, freshness). Discuss model choices and trade-offs.
Describe how to handle low-latency inference, distributed training, and A/B testing. Mention infrastructure like feature stores and model servers.
Cover offline metrics (e.g., NDCG, AUC), online metrics (e.g., CTR, watch time), and how to detect and mitigate biases. Explain the experimentation cycle.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.