About the job
Global E-Commerce Content Recommendation team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is fast-pacing, collaborative and impact-driven.
Responsibilities
Drive the development of industry-leading recommendation systems that elevate user experience, strengthen platform safety, and empower a vibrant content ecosystem.
Explore generative recommendation techniques, including Diffusion Models, prompt learning, and multimodal content generation, to unlock new capabilities in content discovery.
Build multi-model and cross-scenario systems enabling unified recommendation across livestreams, short videos, and search.
Deliver impactful, end-to-end machine learning solutions that tackle high-priority product challenges related to content understanding, LLMs, robustness, and fairness.
Own and optimize the full-stack ML pipeline—from algorithm design to system infrastructure—to continuously push the boundaries of recommendation performance.
Collaborate with cross-functional teams to craft innovative product strategies and develop intelligent solutions that fuel TikTok’s growth in key global markets.
Qualifications
Minimum
Currently pursuing a Bachelor’s or Master’s degree with a background in computer science, machine learning, or similar fields; Good knowledge of theoretical and empirical research in addressing research problems; Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch,TensorFlow) and familiarity with deep neural network architectures. Able to commit to working for 12 weeks during Summer 2026
Preferred
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.