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

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.

We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts. 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).

Responsibilities 1. Build and optimize cross-scenario shared Foundation Models to enable unified modeling and efficient inference. 2. Advance the event-sequence-driven generative recommendation paradigm, integrating multimodal understanding and generative capabilities. 3. Apply LLM technologies across retrieval, ranking, and re-ranking stages; participate in model training, inference optimization, and system co-design. 3. Explore the integration of LLMs / VLMs with recommendation systems to develop adaptive and evolving intelligent recommenders. 4. Research end-to-end generative recommendation and system optimization methods that balance efficiency and user experience.