Large Recommendation Model Algorithm Engineer Intern (Global E-Commerce) - 2027 Start (PhD)

About TikTok

The E-commerce Recommendation Foundation team is dedicated to building the next-generation recommendation intelligence.

We aim to develop a unified Foundation Model that supports multi-business and multi-scenario recommendation systems, covering the full pipeline from retrieval and ranking to re-ranking, and driving a comprehensive upgrade in intelligence and generative capability.

We believe the future of recommendation systems goes beyond predicting click-through rates — it lies in understanding the relationship between people and content, and in generating new connections.

The team is exploring an event-sequence-driven generative recommendation paradigm, deeply integrating large language models (LLMs), multimodal understanding, reinforcement learning, and system optimization to advance recommendation systems toward general-purpose intelligent agents.

We value original exploration and encourage both research thinking and engineering excellence.

Every team member is empowered to propose hypotheses and validate ideas in an open environment — your code and papers may help define the next paradigm of recommendation systems.

We seek individuals with a general intelligence mindset to join us in redefining the future of recommendation.

Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

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.

Requirements

• Currently Pursuing a PhD 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.

Preferred

• Experience in large-scale recommendation system development or large-model training, with notable technical achievements in a sub-area.
• Research experience or publications in LLMs, multimodal learning, reinforcement learning, or generative recommendation.
• Familiarity with pre-training and post-training processes for large language models (LLMs) or Foundation Models.

Application details

• Applications will be reviewed on a rolling basis, so we encourage you to apply early.
• Please clearly state your availability in your resume (Start date, End date).