Research Scientist, Generative Recommendation & LLMs

TikTok; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Job summary

TikTok is hiring for an advanced AI/ML role on the E-commerce Recommendation Foundation team in San Jose, CA. The team focuses on building a unified Foundation Model for multi-business, multi-scenario recommendation systems, spanning retrieval to re-ranking with generative capabilities.

Responsibilities include building cross-scenario models, advancing generative sequences, applying LLM tech across stages, and exploring LLM/VLM integrations to create adaptive, intelligent recommenders while

Qualifications

• MS/PhD in Computer Science, related technical field or equivalent industrial research experience.
• Solid theoretical foundation in machine learning, deep learning, or information retrieval.
• Proficiency in Python and familiarity with mainstream deep learning frameworks (e.g., PyTorch).
• Strong passion for intelligent recommendation systems and a self‑driven research mindset.

Responsibilities

• Build and optimize cross-scenario shared Foundation Models to enable unified modeling and efficient inference.
• Advance the event-sequence-driven generative recommendation paradigm, integrating multimodal understanding and generative capabilities.
• Apply LLM technologies across retrieval, ranking, and re-ranking stages; participate in model training, inference optimization, and system co-design.
• Explore the integration of LLMs / VLMs with recommendation systems to develop adaptive and evolving intelligent recommenders.
• Research end-to‑end generative recommendation and system optimization methods that balance efficiency and user experience.

Skills

Machine Learning

Information Retrieval

Python

Research mindset

Education

MS/PhD in Computer Science or related field

Tools

PyTorch

Job description

TikTok is hiring for an advanced AI/ML role on the E-commerce Recommendation Foundation team in San Jose, CA. The team focuses on building a unified Foundation Model for multi-business, multi-scenario recommendation systems, spanning retrieval to re-ranking with generative capabilities.

Responsibilities include building cross-scenario models, advancing generative sequences, applying LLM tech across stages, and exploring LLM/VLM integrations to create adaptive, intelligent recommenders while