The Recommendation Foundation team within TikTok’s Data – Global E-commerce organization is dedicated to building shared Recommendation Foundation Models across scenarios.
We are exploring an event-sequence-driven generative recommendation paradigm that deeply integrates large language and vision-language models (LLMs/VLMs), multimodal understanding, reinforcement learning, and system optimization, advancing recommendation systems beyond click prediction toward general-purpose recommendation agents.
We believe the future of recommendation is not only about predicting clicks, but about understanding the relationships between people and content and generating new connections.
We value original exploration and encourage research thinking and engineering practice equally.
Every team member can propose hypotheses and validate ideas in an open environment;
your code and publications may help shape the next generation of recommendation systems.
We are looking for people with a general-intelligence mindset to redefine recommendation with us.
We are looking for talented individuals to join us for an internship.
PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.
Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).
Responsibilities:
1. Participate in the full training lifecycle of Recommendation Foundation Models, including pre-training, mid-training, and post-training.
2. Design and train multimodal semantic tokenizers for recommendation items, leveraging multimodal foundation models to encode rich item content into discrete semantic tokens and raise the performance ceiling of Recommendation Foundation Models.
3. Develop LLM-native recommendation by incorporating recommendation tasks directly into large language model training and leveraging world knowledge to improve recommendation quality.
4. Build the next generation of recommendation systems powered by Recommendation Foundation Models, spanning retrieval, ranking, and end-to-end generative recommendation.