The Machine Learning Engineer interview process typically begins with a technical screening focused on data structures and algorithms, followed by a ML fundamentals round to test your grasp of theory like bias-variance and loss functions. The core of the process is the ML System Design round, where you must architect an end-to-end pipeline covering data ingestion, model training, and production deployment. This is usually rounded out by a past projects deep-dive to evaluate your hands-on experience with real-world data messes and a behavioral interview to ensure you can collaborate effectively across cross-functional teams.