Foundation Model Engineer - Generative Recommendation

TikTok; Singapore; On-site; SGD 140,000 - 210,000; Full time

Job summary

TikTok is building the next-generation recommendation intelligence within the E-commerce Recommendation Foundation team, focusing on unified models for multi-business scenarios and end-to-end generative capabilities. We pursue cross-disciplinary research and engineering excellence, inviting graduates with strong ML foundations to contribute to fast, impactful developments.

Successful candidates will work on MLOps-friendly model training, inference optimization, and integration of LLMs with

Qualifications

• PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• 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

Machine Learning / DL

Self-driven research mindset

Strong problem-solving

Education

PhD in Computer Science/Software Engineering/EE, or related

Tools

PyTorch

LLM knowledge

Job description

TikTok is building the next-generation recommendation intelligence within the E-commerce Recommendation Foundation team, focusing on unified models for multi-business scenarios and end-to-end generative capabilities. We pursue cross-disciplinary research and engineering excellence, inviting graduates with strong ML foundations to contribute to fast, impactful developments.

Successful candidates will work on MLOps-friendly model training, inference optimization, and integration of LLMs with