Research Scientist - Generative Recs & Foundation Models

TikTok; San Jose (CA); On-site; USD 156,000 - 388,000; Full time

Benefits offered by this job

Medical benefits

401(k)

Parental leave

Disability coverage

Life insurance

Wellbeing programs

Paid holidays

Paid sick days

Paid personal time

Job summary

TikTok is seeking a Research Scientist for its E-Commerce Recommendation Foundation in San Jose. You will advance cross-scenario Foundation Models, integrate multimodal understanding, and drive efficient inference for unified modeling.

The role emphasizes building end-to-end generative recommendation methods, exploring LLMs and VLMs within retrieval, ranking, and system co-design, to balance performance and user experience.

Qualifications

• MS/PhD in Computer Science or related field or equivalent research experience.
• Strong foundation in ML, DL, or IR.
• Proficiency in Python and familiarity with PyTorch.
• 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

Python

PyTorch

ML fundamentals

Information Retrieval

Research mindset

Education

MS/PhD in Computer Science or related field

Tools

PyTorch

Job description

TikTok is seeking a Research Scientist for its E-Commerce Recommendation Foundation in San Jose. You will advance cross-scenario Foundation Models, integrate multimodal understanding, and drive efficient inference for unified modeling.

The role emphasizes building end-to-end generative recommendation methods, exploring LLMs and VLMs within retrieval, ranking, and system co-design, to balance performance and user experience.