Large Recommend Model Algorithm Engineer - Global E-Commerce

Responsibilities

About the Team
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.

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.

Qualifications

Minimum Qualifications
- Solid theoretical foundation in machine learning, deep learning, or information retrieval.