Research Scientist - TikTok Next Generation Recommendation

About The Team:

You will be joining TikTok's Next-Generation Recommendation team, focused on pioneering cutting-edge recommendation systems powered by advanced large-model technologies. This team is dedicated to advancing TikTok’s personalized content discovery and user experiences by harnessing the power of large models and leveraging massive user data to build revolutionary recommendation technologies. By pushing the boundaries of deep learning and large-scale system design, we strive to achieve breakthroughs in recommendation accuracy, user engagement, and scalability to serve billions of users worldwide.

We are looking for interdisciplinary talents, such as recommendation algorithm experts who are not only deeply familiar with existing practices in recommendation systems but also bring unique and innovative perspectives to recommendation methodologies. Additionally, We are seeking experts in the field of multimodal large models to advance the precision of recommendation systems in capturing user interests through deeper content understanding as well as AI infrastructure engineers who excel in optimizing model performance to its fullest potential. These individuals should be passionate about developing next-generation intelligent and user-centric recommendation systems capable of deeply understanding user interests. In this role, you will work closely with cross-functional teams to tackle complex personalization challenges and drive the evolution and scalability of recommendation systems powered by advanced large models.



Responsibilities

1- Design and develop next-generation large-scale recommendation systems optimized for personalized, engaging, and scalable user experiences.



2-Leverage state-of-the-art machine learning and deep learning techniques, including large model technologies（LLM and MLLM, etc), to enhance recommendation performance and accuracy.



3- Collaborate with cross-disciplinary teams, including infrastructure engineers, PMO, and researchers, to create advanced systems that improve recommendation relevance, diversity, and user engagement.