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Design a real-time game recommendation system

RobloxMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenML System Design

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Design a real-time game recommendation system evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design a real-time game recommendation system for a platform with millions of games and tens of millions of users. Focus on the system architecture, not ML model details. Describe:

  1. end-to-end request flow and latency SLOs;
  2. online/offline data ingestion (user events, game metadata) and schemas;
  3. streaming feature computation and a real-time feature store;
  4. candidate generation and ranking service interfaces;
  5. caching strategy and freshness/TTL;
  6. user personalization state and session handling;
  7. scalability plans (QPS estimates, partitioning, replication, storage);
  8. experimentation and rollout (A/B, feature flags);
  9. cold-start strategies;
  10. failure modes, consistency, and backfill/lag recovery;
  11. privacy, security, and abuse prevention;
  12. monitoring, alerting, and cost controls.