Start by clarifying functional and non-functional requirements, then propose a high-level architecture that scales from 1M to 1B users. Emphasize ML components like ranking and personalization, and discuss trade-offs in storage, fan-out, and model serving.
Pro tip: For an ML Engineer role at Apple, highlight privacy-preserving techniques (e.g., on-device ranking, federated learning) and how ML models are updated and served at scale without compromising user trust.
Ask questions to define core features (posting, feed generation, following, interactions) and non-functional needs (latency, availability, consistency). Establish scale targets from 1M to 1B users.
Sketch components: API gateway, user/post services, graph service, feed service, ML ranking service, and storage layers (SQL/NoSQL, cache, blob). Define data models for users, posts, follows, and interactions.
Discuss fan-out on write vs. read, sharding, replication, and caching. Explain how the architecture evolves: from monolithic to microservices, from single DB to distributed stores, and from simple ranking to ML-driven feeds.
Detail the ML pipeline: candidate generation, feature engineering, model training (e.g., ranking models), and serving (real-time inference). Discuss online/offline evaluation and feedback loops.
Describe how components scale at 1B users: multi-region deployment, CDN, data partitioning, model versioning, A/B testing, and monitoring. Address privacy, security, and cost.
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