Practice 281 real ML System Design interview questions for 2026. Covers companies like OpenAI, Meta, Amazon, Anthropic, and Google. Real questions from actual interviews with detailed solutions. This collection targets ML System Design interview questions and interview preparation for roles that must bridge modeling, data engineering, and production reliability. What’s distinctive: expect LLM- and RAG-focused problems (inference efficiency, retrieval, hallucination controls), feature-store and data-lineage designs, real-time versus batch inference trade-offs, GPU/TPU serving patterns (batching, KV-caches), monitoring for data and concept drift, and production CI/CD for models. Interviewers evaluate your ability to clarify requirements, choose constraints-aware architectures, reason about cost and latency, and specify metrics and guardrails for safety and observability. To prepare, practice drawing layered diagrams (ingestion, storage, feature pipeline, training, registry, serving, monitoring), rehearse trade-offs aloud, and build short writeups outlining metrics, retraining strategy, and rollback/alerting plans. Focus on clear assumptions, end-to-end reproducibility, and concrete operational details that show you can ship and maintain ML at scale.