About the job
Global E-Commerce Content Recommendation team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is fast-pacing, collaborative and impact-driven. In today’s content-driven commerce landscape, traditional collaborative filtering and supervised learning methods are no longer sufficient. We’re actively exploring how large language models (LLMs) and generative AI can fundamentally transform the recommendation process: from retrieval to ranking, and from static listings to dynamic, generative user-item interactions.
Responsibilities
Design algorithms and systems that leverage LLMs and generative models for content-to-commerce matching, product summarization, etc
Explore novel architectures and strategies for generative recommendation systems
Contribute to the research community via internal papers, patents, or external publications
Drive scientific rigor while balancing real-world constraints
Qualifications
Minimum
Individuals who are completing or have recently completed a PhD degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related technical field
Experience with LLMs (e.g., GPT, LLaMA, PaLM) or diffusion/generative models, through research or practical application
Proficient coding skills in Python and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch
Demonstrated ability to conduct rigorous research and analyze large-scale data
Strong problem-solving skills and a high sense of ownership
Preferred
Publications in ML/AI conferences (e.g., NeurIPS, ICML, ACL, SIGIR, KDD, CVPR, RecSys)
Experience with recommendation systems, retrieval models, or multi-modal learning
Familiarity with building and deploying real-time, scalable ML systems in production
Background in e-commerce or related applied AI research domains