TikTok Machine Learning interview questions often focus on building and evaluating large-scale, multi-modal recommendation and personalization systems. Interviewers typically evaluate core ML fundamentals (modeling, optimization, evaluation metrics), software and data engineering skills (feature engineering, SQL, pipelines), experiment design and metrics literacy (A/B testing, causality, launch metrics), and product judgment that connects model choices to user experience and business tradeoffs. Expect questions that probe scalability, latency trade-offs, fairness and privacy considerations, and the ability to translate ambiguous product goals into measurable ML solutions. For interview preparation, plan for a mix of screens: recruiter/phone screens, technical coding or modeling rounds, ML-system or architecture design, and behavioral/product interviews. Practice end-to-end case studies (recommendation pipelines, online inference, and offline evaluation), refresh statistics and experiment design, rehearse clear tradeoff explanations, and prepare concise project narratives showing impact. Time-boxed mock interviews and a small portfolio of reproducible projects will help demonstrate both depth and the pragmatic engineering needed for success.