Research Scientist — Generative E‑Commerce Recommendations

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

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with match

Parental leave

Disability insurance

Wellbeing benefits

Paid holidays

Job summary

TikTok is expanding its E-commerce Recommendation Foundation team to build next-generation foundation models for cross-scenario use, enabling unified modeling and efficient inference. The role emphasizes LLM/VM integration, retrieval and ranking optimization, and advancing generative recommendation with scalable systems.

You will contribute to research and engineering efforts with a focus on practical impact and innovation in a fast-moving tech environment.

Qualifications

• MS or PhD in CS or related field, or equivalent industrial research experience.
• Strong ML/DL theory and information retrieval fundamentals.
• Proficiency in Python and PyTorch.
• 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 the event-sequence-driven generative recommendation paradigm, integrating multimodal understanding and generative capabilities.
• Apply LLM technologies across retrieval, ranking, and re-ranking; contribute to training and inference optimization.
• Explore integration of LLMs/VLMs with recommendation systems for adaptive intelligent recommenders.
• Research end-to-end generative methods balancing efficiency and user experience.

Skills

MS/PhD in Computer Science

ML/DL theory

Python

PyTorch

Research mindset

Education

MS/PhD in Computer Science or related field

Tools

PyTorch

Job description

TikTok is expanding its E-commerce Recommendation Foundation team to build next-generation foundation models for cross-scenario use, enabling unified modeling and efficient inference. The role emphasizes LLM/VM integration, retrieval and ranking optimization, and advancing generative recommendation with scalable systems.

You will contribute to research and engineering efforts with a focus on practical impact and innovation in a fast-moving tech environment.