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Design a recommendation system

MetaMachine Learning Engineer · Onsite
HardMachine Learning EngineerOnsiteSystem Design

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This question evaluates competence in designing large-scale recommendation systems and ML engineering skills such as candidate generation, feature store design (offline/online and point-in-time correctness), real-time signal ingestion, ranking and re-ranking, online exploration, cold-start strategies, feedback-bias mitigation, experimentation, and operational reliability. It is commonly asked to assess the ability to make architecture-level trade-offs for scalability, latency and throughput targets, multi-objective optimization, data governance and outage fallback behavior; it falls under the System Design and Machine Learning domain and requires practical, architecture-level application with conceptual trade-off reasoning rather than low-level implementation detail.

Design a large-scale recommendation system for a consumer app’s home feed. Describe the end-to-end architecture including candidate generation, feature stores, real-time signals ingestion, ranking, re-ranking, and online exploration (e.g., multi-armed bandits). Explain strategies for cold start (new users/items), feedback loops and bias mitigation, A/B testing, latency/throughput targets, data privacy, and fallback behavior during outages.