Research Scientist, Generative E‑Commerce Recommendations

TikTok; Seattle (WA); On-site; USD 154,000 - 301,000; Full time

Job summary

TikTok is building the next generation of recommendation intelligence in Seattle, focusing on unified Foundation Models to serve multiple businesses and scenarios. The role encompasses retrieval, ranking, and re-ranking within a unified framework, leveraging LLMs, multimodal understanding, and reinforcement learning to push the boundaries of intelligent recommendations.

The team emphasizes open exploration, rigorous research, and engineering excellence, inviting researchers with a strong

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

Python

ML/IR fundamentals

Research mindset

CS/MS/PhD background

Education

MS/PhD in Computer Science or related field

Tools

PyTorch

Job description

TikTok is building the next generation of recommendation intelligence in Seattle, focusing on unified Foundation Models to serve multiple businesses and scenarios. The role encompasses retrieval, ranking, and re-ranking within a unified framework, leveraging LLMs, multimodal understanding, and reinforcement learning to push the boundaries of intelligent recommendations.

The team emphasizes open exploration, rigorous research, and engineering excellence, inviting researchers with a strong