Machine Learning Engineer, E-commerce Recommendation Foundation - USDS

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.
- 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.