(General Hire) Research Scientist Graduate (TikTok Recommendation-Next Gen Recommendation) - 2026 Start (PhD)

At TikTok, we are building the next-generation recommendation systems to deliver highly personalized and engaging content to billions of users worldwide. The TikTok Recommendation team pioneers cutting-edge technologies by harnessing the power of large-scale models and massive user data to drive breakthrough innovations in accuracy, scalability, and user experience.



As a PhD new graduate, you will join a fast-paced, research-inspired engineering team focused on applying large model technologies to real-world recommendation scenarios. Through our General Hire track, you'll have the opportunity to explore multiple research and engineering directions across teams including but not limited to:

- Generative Recall

- Large Generative Reranking Models

- Agentic AI

- MLLM Pre-training & Post-training

- VLM + LLM for Cold Start

- Lifelong Sequence Modeling

- LLM Applications for Push & EDM



We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.



Successful candidates must be able to commit to an onboarding date by end of year 2026.



We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume.



Applications will be reviewed on a rolling basis. We encourage you to apply early.



Responsibilities:

- Research and develop next-generation recommendation models leveraging LLMs, MLLMs, and generative techniques.

- Design and optimize large-scale systems for multimodal content understanding and personalized ranking.

- Collaborate with cross-functional teams (infra, PMs, research) to turn prototypes into scalable solutions.

- Stay at the forefront of academic advancements and apply SOTA ideas to solve large-scale, real-world recommendation challenges.