Machine Learning Engineer Graduate
TikTok USDS Joint Venture
About the Role
As part of the Tech and Product team of TikTok USDS, you will build and scale high-impact Machine Learning solutions, including recommendation engines, search relevance, and e-commerce systems. These solutions are designed to deliver personalized, high-performing experiences while ensuring world-class data governance, privacy compliance, and system security for US users. This is a graduate role with opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must commit to an onboarding date by the end of the year.
Key Responsibilities
- Design, build, train, and evaluate machine learning models for recommendation systems, search ranking, query understanding, or conversion prediction.
- Implement efficient pipelines for feature extraction, model training, evaluation, offline-to-online deployment, and A/B testing.
- Process and analyze massive user behavior data and catalog logs using distributed systems to construct real-time and offline features.
- Optimize online inference latency and system throughput for high-QPS production microservices.
- Conduct continuous A/B testing, analyze online metrics, identify bottleneck areas, and iterate on models to drive business goals.
Requirements & Tech Stack
- Individuals completing or recently completed a PhD's degree in Computer Science, Engineering, Math, Statistics, or a related discipline.
- Solid background in Data Structures, Algorithms, Computer Systems, and Software Design.
- Strong theoretical understanding and practical knowledge of machine learning concepts.
- Experience in recommendation system, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.
- Publications at KDD, NeurlPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RECSYS and related conferences/journals, or experience in data mining/machine learning competitions such as Kaggle/KDD-cup etc.
Qualifications
- PhD in Computer Science, Engineering, Math, Statistics, or a related discipline.
- Strong foundational knowledge in core computer science principles.
- Proven understanding of machine learning principles.