Research Scientist Graduate (TikTok Recommendation-Agentic AI) - 2026 Start (PhD)

Welcome to TikTok's Agentic Recommendation team, where we're reimagining the future of recommendation systems. Our mission is to build world-class, end-to-end recommender systems that go beyond prediction to reasoning, alignment, and autonomy. We're integrating cutting-edge techniques such as multimodal large language models (MLLMs), reinforcement learning, and agentic alignment to drive next-generation user experiences at scale. If you're passionate about pushing the boundaries of recommender systems, join us to explore, innovate, and help shape the future for billions of users around the world. 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: - 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. Minimum Qualifications: - Final-year Ph.D. candidate or recent Ph.D. graduate in Computer Science or a related field. - Strong knowledge and hands-on experience in at least one of the following areas: reinforcement learning, agent development, or LLM post-training. - Passion for exploring cutting-edge research topics and bridging theory with practice. Preferred Qualifications: - Publications in accredited academia 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.