Machine Learning Engineer Graduate (Tech and Product) - 2027 Start (PhD)

Job description

## About the role TikTok USDS Joint Venture is looking for a PhD‑qualified Machine Learning Engineer Graduate to join the Tech and Product team. You will help build and scale high‑impact ML solutions such as recommendation engines, search relevance, and e‑commerce systems that deliver personalized experiences while meeting strict data‑governance, privacy, and security standards for US users. ## What you'll do - Design, build, train, and evaluate machine‑learning models for recommendation, search ranking, query understanding, and conversion prediction. - Implement efficient pipelines for feature extraction, model training, evaluation, offline‑to‑online deployment, and A/B testing. - Process massive user‑behavior and catalog logs using distributed systems to create real‑time and offline features. - Optimize online inference latency and throughput for high‑QPS production micro‑services. - Conduct continuous A/B testing, analyze online metrics, identify bottlenecks, and iterate on models to meet business goals. ## Required qualifications - Currently completing or recently completed a PhD in Computer Science, Engineering, Mathematics, Statistics, or a related field. - Strong foundation in data structures, algorithms, computer systems, and software design. - Solid theoretical and practical knowledge of machine‑learning concepts. - Prior internship or project experience in recommendation systems, search engines, computational advertising, or e‑commerce AI is a plus. - Experience with distributed computing frameworks (e.g., Spark, Flink) is desirable. ## Benefits - Competitive base salary ($136,800 – $259,200 USD) with potential bonuses, RSUs, and comprehensive benefits. - Day‑one medical, dental, and vision coverage, 401(k) match, paid parental leave, disability and life insurance, and wellbeing programs. - Generous paid time off: 10 holidays, 10 sick days, and 17 personal days per year (prorated). - Opportunity to work on cutting‑edge ML products that impact millions of users. - Collaborative in‑person environment with a shift to a fully on‑site schedule (up to 5 days a week).

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