Foundation Models Scientist for E‑Commerce Recommendations

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

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) match

Paid parental leave

Paid holidays

Job summary

TikTok is a global tech company seeking researchers to advance its recommendation intelligence by building a unified Foundation Model for cross-business use cases. You will drive research and engineering to improve retrieval, ranking, and re-ranking, while exploring generative capabilities and multimodal integration.

The role emphasizes a strong research mindset and collaboration, with opportunities to publish, experiment, and contribute to shaping next-generation recommender systems.

Qualifications

• MS/PhD in CS or related field or equivalent research experience.
• Strong foundation in ML, DL, or information retrieval.
• Proficiency in Python and PyTorch.
• Strong passion for intelligent recommendation systems and a self-driven research mindset.

Responsibilities

• Build and optimize cross-scenario Foundation Models for unified modeling and efficient inference.
• Advance event-sequence-driven generative recommendation with multimodal understanding.
• Apply LLM technologies across retrieval, ranking, and re-ranking; contribute to training and inference optimization.
• Explore integration of LLMs/VLMs with recommender systems for adaptive intelligent agents.
• Research end-to-end generative recommendation and system optimization methods balancing efficiency and UX.

Skills

MS/PhD in CS

ML / DL / IR

Python & PyTorch

Research mindset

Education

MS/PhD in Computer Science

Tools

PyTorch

Job description

TikTok is a global tech company seeking researchers to advance its recommendation intelligence by building a unified Foundation Model for cross-business use cases. You will drive research and engineering to improve retrieval, ranking, and re-ranking, while exploring generative capabilities and multimodal integration.

The role emphasizes a strong research mindset and collaboration, with opportunities to publish, experiment, and contribute to shaping next-generation recommender systems.