Senior ML Engineer - Generative Recommendation Foundation

TikTok USDS Joint Venture; Seattle (WA); On-site; USD 198,000 - 416,000; Full time

Job summary

TikTok USDS Joint Venture LLC is advancing a next-generation recommendation intelligence foundation. We build unified Foundation Models for multi-business and multi-scenario needs, spanning retrieval to re-ranking, and driving upgrades in intelligence and generative capability.

The team encourages original exploration, empowering researchers and engineers to validate ideas in an open environment. Join us to redefine the future of recommendation technologies.

Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.
• 3+ years of experience 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.
• Apply R25 techniques across retrieval, ranking, and re-ranking stages; participate in model training and inference optimization.
• Explore integration of LLMs/VLMs with recommendation systems to develop adaptive and evolving intelligent recommenders.
• Research end-to-end generative recommendation and system optimization methods balancing efficiency and user experience.

Skills

Machine learning

Deep learning

Information retrieval

Education

Bachelor's or Master's in CS/ML or related

Tools

PyTorch

Python

Job description

TikTok USDS Joint Venture LLC is advancing a next-generation recommendation intelligence foundation. We build unified Foundation Models for multi-business and multi-scenario needs, spanning retrieval to re-ranking, and driving upgrades in intelligence and generative capability.

The team encourages original exploration, empowering researchers and engineers to validate ideas in an open environment. Join us to redefine the future of recommendation technologies.