Machine Learning Engineer ML System Design Interview Questions

Machine Learning Engineer candidates get 207 of the ML system design questions on file, and the framing assumes you own the pipeline rather than consult on it. OpenAI accounts for 19 of them and Meta 18, with Snapchat and Amazon at 12 each and Microsoft, Google, Pinterest and Apple behind, so ranking and recommendation dominate: a short-video recommender tuned for short-term interest, thumbnail selection across a streaming catalogue, request detection and labelling at global scale. Interviewers push on the parts a model card never shows. Where does the training data come from, who labels it, how do you keep the labels honest when the data arrives dirty, how fresh do features need to be, when do you retrain, and what tells you the model has drifted before a business metric does. Orchestration comes up on its own, as does agent-shaped work such as a multi-agent document search and visualisation product. This is an onsite category first: 119 questions were asked in an onsite loop against 72 in a technical screen, and 70 sit in the hard band. Every one carries a written solution, and 61% are readable without premium.