Meta Machine Learning Engineer interview questions typically probe both algorithmic skill and practical ML judgment. At Meta you should expect a mix of coding (data structures and algorithms), applied ML and modeling questions, ML-system design, and behavioral/leadership rounds that focus on impact, collaboration, and product thinking. What’s distinctive is the emphasis on production-ready thinking: interviewers evaluate how you translate models into scalable systems, choose metrics, reason about data and bias, and trade off latency, cost, and reliability in real-world settings. Recent pilots also include AI-assisted coding components in some interviews, so being fluent with modern developer workflows can help. For interview preparation, prioritize three threads: sharpen algorithmic coding (medium-to-hard problems), deepen practical ML fundamentals (evaluation metrics, debugging, feature engineering, model degradation), and practice end-to-end ML system design at scale (data pipelines, monitoring, deployment). Prepare STAR stories that show ownership and cross-team impact, and rehearse clear, structured explanations of trade-offs. Expect a timed loop of 4–6 focused interviews and a hiring committee review, so consistent performance across rounds matters more than a single standout answer.