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.
About the team and the role:
Our Recommendations team works on delivering recommendations at scale and in near real time to our buyers on our eBay e-commerce marketplace 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 state-of-the-art 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 setting.
We are building cutting edge recommender systems powered by the latest ML, NLP, LLM/GenAI/RAG and AI technologies! We are looking for a top-tier applied researcher who can lead and drive forward our research in personalization, with the business goal of enabling personalized discovery of eBay unique inventory through dynamic feeds, both for eBay enthusiasts as well as first time visitors.
The scope of research includes 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. Furthermore, we are looking to expand into video recommendations as well as live commerce, so this is an exciting new area for us to explore.
What you will accomplish:
Work with unique and large data sets of unstructured multimodal data representing eBay's vast and varied inventory, including billions of items and millions of users
Develop and deploy innovative AI models to production which have direct measurable impact on eBay buyers, working with a global team of applied researchers and ML engineers
Drive marketplace GMB as well as advertising revenue via organic and sponsored recommendations
Publish research work in Tier 1 conferences as well as write IP and tech blogs
What you will bring:
BS in Computer Science or related area with 5+ years of relevant work experience in Machine Learning / AI / NLP / LLM. MS or PhD preferred.
Experience with building personalized recommender systems or other forms of personalization technology
Python and PyTorch framework
Hands on engineering skills to build and deploy ML models in production on prem or using cloud services
Experience in Java/Scala is a plus
Experience with big data pipelines (Hadoop, Spark) is a plus
Experience with Large Language Models (LLMs), prompt engineering, and GenAI coding assistants is a plus
Links to some of our previous work:
Tech Blog 2025 (Multimodal GenAI)
Tech Blog 2025
(GenAI Agentic Platform)
RecSys 2024 Workshop paper
Google Cloud Blog 2024
eBay Tech Blog 2023
eBay Tech Blog 2022
RecSys 2021 paper
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. We will make every effort to respond to your request for accommodation as soon as possible. View our
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