Meta ML System Design interview questions focus on building production-grade machine learning systems at extreme scale and product impact. What’s distinctive at Meta is the emphasis on end-to-end thinking: interviewers expect you to connect model choices to data pipelines, feature stores, serving architectures, monitoring, and experimentation. You’ll be evaluated on clarifying ambiguous requirements, designing for latency and throughput, handling freshness and drift, quantifying trade-offs (cost, accuracy, fairness), and incorporating privacy and ethical constraints. Expect open-ended prompts tied to real Meta products—feed/recommendation, ads, content moderation, or spam detection—where clear scoping and measurable metrics matter as much as model details. For interview preparation, practice a repeatable framework: clarify goals and constraints, sketch data flow and components, pick concrete storage and serving solutions, deep-dive on bottlenecks, and finish with monitoring, rollout, and rollback strategies. Drill common scenarios (recommenders, real-time inference, feature stores, A/B testing) with timing and trade-off narratives, rehearse quantifying latency and throughput, and run mock interviews that force concise, metric-driven explanations.