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.
Looking for a company that inspires passion, courage and imagination, where you can be part of 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 creating an ambitious and inclusive workplace, join – a company you can be proud to be a part of.
The Product Knowledge and Query Science team at is responsible for creating a product catalog for’s huge inventory, while ensuring that this inventory is shown to our buyers in a relevant and efficient way. The team combines top talent in the fields of Engineering, Science, ML and Operational efficiency.
You’ll be embedded in the Product Knowledge org, shaping how we structure and optimize taxonomy, ontology, and catalog data for next-gen search, recommendations, and AI-driven experiences.
This is a high-agency, startup-style role—if you thrive in fast-moving, product-focused environments and love working closely with AI/ML, Product, and Engineering teams, this is for you.
You will be part of the Tagging & Editorial Data Operations team, which supports our science teams with high-quality human judgment tagging of dataset. You will be responsible for creating training data for AI models, measuring metrics and performing editorial actions on the data supporting some of’s most innovative experiences. The ideal candidate will have a strong sense of ownership, technical expertise, and a deep understanding of data quality.
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