AI Research Lead, Personalization

at  eBay

At eBay, we’re more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.
Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We’re in this together, sustaining the future of our customers, our company, and our planet.
Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

Our Recommendations team works on delivering recommendations at scale and in near real time to our buyers on our website and native app platforms. Recommendations are a core part of how our buyers navigate eBay’s vast and varied inventory. Our team develops innovative recommendations systems, including deep learning based retrieval systems for personalized recommendations, machine learned ranking models, GenAI/LLM powered recommendations, as well as sophisticated MLOps in a high volume traffic industrial e-commerce setting.
We are building groundbreaking recommender systems powered by the latest ML, NLP, LLM/GenAI/RAG and AI technologies! Additionally, we are building production integrations with Google GCP Vertex AI platforms to supercharge our item recommendation algorithms.
We are looking for a top-tier applied researcher who can drive our research in personalization, with the business goal of enabling personalized discovery of eBay unique inventory, both for eBay enthusiasts as well as first time visitors.
The scope of research incudes but is not limited to sophisticated ML models for personalized recall and ranking that uses very large-scale attention models, graph neural networks, few-shot learning, and other techniques to better model factors relevant to people’s interests, cohort memberships, and trends. Also in scope are deep user understanding, advanced user embeddings, interpretable user models, and interest graphs - both global and personal.