Research Scientist - Generative E-Commerce Recommendations

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

Job summary

TikTok is hiring for a research-focused role in San Jose, CA, focused on building a unified Foundation Model for multi-business recommendation systems. The team emphasizes event-sequence driven generative methods, LLMs, multimodal understanding, and system optimization.

The candidate will tackle end-to-end generative recommendation challenges, bridging retrieval, ranking, re-ranking, and large-scale training while contributing to scalable, intelligent agents.

Qualifications

• MS/PhD in CS or related 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 foundation

Python programming

Research mindset

Education

MS/PhD in CS or related field

Tools

PyTorch

Job description

TikTok is hiring for a research-focused role in San Jose, CA, focused on building a unified Foundation Model for multi-business recommendation systems. The team emphasizes event-sequence driven generative methods, LLMs, multimodal understanding, and system optimization.

The candidate will tackle end-to-end generative recommendation challenges, bridging retrieval, ranking, re-ranking, and large-scale training while contributing to scalable, intelligent agents.