TikTok's Agentic Recommendation team is looking to reimagine the future of recommendation systems by integrating cutting-edge techniques such as multimodal large language models (MLLMs), reinforcement learning, and agentic alignment to drive next-generation user experiences at scale.
Requirements
- Strong knowledge and hands-on experience in at least one of the following areas: reinforcement learning, agent development, or LLM post-training.
- Publications in top-tier conferences such as NeurIPS, ICML, ICLR, AAAI, IJCAI, RecSys, KDD, WWW, or WSDM.
- Internship or project experience related to reinforcement learning, agent-based systems, or LLM alignment/post-training.
Responsibilities
- Design and construct reinforcement learning datasets tailored for recommendation scenarios.
- Explore and optimize reinforcement learning algorithms, and develop scalable training and inference frameworks.
- Conduct research on agentic recommendation systems and generative recommendation models.
- Stay up to date to the latest SoTA advancements in relevant areas, and translate research ideas into real-world applications.
Other
- Final-year Ph.D. candidate or recent Ph.D. graduate in Computer Science or a related field.
- Passion for exploring cutting-edge research topics and bridging theory with practice.
- 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.
