Foundation Model Engineer - Generative Recommendation Systems

TikTok; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

TikTok's E-commerce Recommendation Foundation team is building the next generation of recommendation intelligence, aiming to develop a unified Foundation Model for multi-business and multi-scenario systems. The team blends retrieval, ranking, and re-ranking with LLMs and multimodal understanding to advance intelligent agents.

As a graduate, you will tackle bold ideas, engage in research and engineering excellence, and contribute to scalable, end-to-end generative recommendation systems with

Qualifications

• Bachelor's or Master's 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

Deep learning

Research mindset

Education

Bachelor's degree in Software Development/Computer Science/Computer Engineering

Master's degree in Software Development/Computer Science/Computer Engineering

Job description

TikTok's E-commerce Recommendation Foundation team is building the next generation of recommendation intelligence, aiming to develop a unified Foundation Model for multi-business and multi-scenario systems. The team blends retrieval, ranking, and re-ranking with LLMs and multimodal understanding to advance intelligent agents.

As a graduate, you will tackle bold ideas, engage in research and engineering excellence, and contribute to scalable, end-to-end generative recommendation systems with