Foundation Model Engineer — Generative Recommendation

TIKTOK PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

TikTok is expanding its E-commerce Recommendation Foundation team in Singapore, building next-generation foundation models for cross-scenario recommendations. You will work at the intersection of retrieval, ranking, and generation with large language models and multimodal understanding.

Join a research-forward engineering culture that values curiosity, experimentation, and impact, and contribute to systems that adapt to user needs with efficiency and scalability.

Qualifications

• Solid theoretical foundation in machine learning, deep learning, or information retrieval.
• Proficiency in Python and PyTorch or similar DL frameworks.
• Strong passion for intelligent recommendation systems and a self-driven research mindset.

Responsibilities

• Build and optimize cross-scenario shared Foundation Models for unified modeling.
• 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 theory

Research mindset

Tools

PyTorch

Job description

TikTok is expanding its E-commerce Recommendation Foundation team in Singapore, building next-generation foundation models for cross-scenario recommendations. You will work at the intersection of retrieval, ranking, and generation with large language models and multimodal understanding.

Join a research-forward engineering culture that values curiosity, experimentation, and impact, and contribute to systems that adapt to user needs with efficiency and scalability.