TikTok is looking to improve its live recommendation systems to enhance user experience, content ecosystem, and platform security in live streaming. The company aims to deliver end-to-end machine learning solutions for real-time recommendation and personalization of live content, optimizing algorithms and infrastructure to boost performance and grow the live streaming ecosystem in key markets.
Requirements
- Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch,TensorFlow) and familiarity with deep neural network architectures.
- Research experience in one or more of the following fields: applied machine learning, machine learning infrastructure, large-scale recommendation system, market-facing machine learning product;
- Strong first-author publications record in top AI conferences or journals(e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL etc.);
- Proficient in C/C++, Python, and shell programming languages, and have a deep understanding of data structure and algorithm design;
- Internship experience in an AI research organization.
Responsibilities
- Build industry-leading live recommendation systems, improving user experience, content ecosystem, and platform security in live streaming;
- Deliver end-to-end machine learning solutions to address critical product challenges in real-time recommendation and personalization for live content;
- Own the full-stack machine learning system and optimize algorithms and infrastructure to improve live recommendation performance;
- Work with cross-functional teams to design product strategies and build solutions to grow TikTok’s live streaming ecosystem in important markets.
Other
- Currently pursuing a Master degree with a background in computer science, machine learning, or similar fields;
- Good knowledge of theoretical and empirical research in addressing research problems;
- Able to commit to working for 12 weeks during Summer 2026
- Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply.
- Applications will be reviewed on a rolling basis.
