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 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.

What you will accomplish:

• Define research directions that will Influence how people discover eBay's inventory relevant to their interests and passions
• 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

What you will bring:

• PhD in Computer Science or related area with 10 years of relevant work experience in Machine Learning / AI
• Experience with building personalized recommender systems or other forms of personalization technology
• Strong track record of applied research supported by publications, blog articles or other demonstrations of knowledge of the field
• Hands on engineering skills to build and deploy ML models on prem or using cloud services
• Experience acting as research lead, articulating research strategy

Links to some of our previous work:

• Google Cloud Blog 2024
• eBay Tech Blog 2023
• RecSys 2022 Workshop paper
• eBay Tech Blog 2022
• RecSys 2021 paper

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eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at [email protected]. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities.

Jobs posted with location as "Remote - United States (Excludes: HI, NM)" excludes residents of Hawaii and New Mexico.

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