Applied Researcher

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

Looking for a company that inspires passion, courage and creativity, where you can be on the team shaping the future of global commerce? Want to shape how millions of people buy, sell, connect, and share around the world? If you're interested in joining a purpose driven community that is dedicated to crafting an ambitious and inclusive work environment, join eBay – a company you can be proud to be with.

The AI/ML team within eBay's Selling organization builds machine learning and generative AI systems that help sellers make confident pricing and listing decisions. We combine rigorous econometric methodology with production ML engineering — moving research from theoretical grounding through reproducible prototypes to seller-facing features used across eBay's global marketplace.

As an Applied Researcher, you will design and advance our research agenda across machine learning and generative AI, contributing to both our price guidance modeling and the team's expanding selling-agent platform. You will work with stakeholders across Product, Engineering, and Data to translate research findings into systems that reach sellers worldwide. Performance, reliability, and measurable business impact are central to this work, as it touches the pricing and selling decisions at the core of eBay's marketplace.

Problems we are working on:
- Designing price guidance systems that separate confounded marketplace correlations from true seller-controllable price elasticity.
- Building LLM-based selling agent components that route seller queries, invoke specialized ML tools, and synthesize actionable listing advice.
- Developing evaluation frameworks for generative AI systems: synthetic dataset construction, LLM-as-judge pipelines, and human adjudication protocols.
- Applying knowledge distillation to replace high-latency LLM classifiers with lightweight, cost-efficient encoder models that maintain production accuracy.
- Researching feedback loop dynamics between AI price recommendations and marketplace outcomes.