How to Crack Machine Learning System-Design Interviews

Machine learning system design interviews are a core part of the hiring process at major tech companies, testing a candidate's ability to handle ambiguity and translate business goals into ML solutions. A strong performance requires leading the conversation, asking thoughtful questions, and understanding system-level trade-offs. The interview should follow a clear structure, starting with problem clarification and metrics, moving to high-level architecture, data, features, modeling, and finally A/B testing and deployment. To prepare, candidates should solidify ML fundamentals and study numerous real-world case studies and system design blogs to recognize common patterns.