Large Recommendation Model Algorithm Engineer - Global E-Commerce

Responsibilities

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.
- 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 Qualifications
- 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.

Responsibilities

Responsibilities

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

Qualifications

Minimum Qualifications
- 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 Qualifications
- 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.