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 role & team:

Advertising is one of the fastest growing areas in eBay, defining the future of our company. As the digital advertising industry rapidly evolves, ecommerce advertisers are increasingly finding greater value with eBay. This shift away from traditional platforms like Google and Facebook presents a tremendous opportunity for us. Our advertising initiatives improve eBay’s ecommerce by helping sellers move inventory and surfacing high-quality items for buyers.

Our team is at the forefront of building end-to-end ML and data-driven advertising systems that power both ad serving and advertiser-side optimization. We develop sophisticated recommendation models to drive marketplace monetization and build relevant buyer experiences. Additionally, we provide intelligent, automated mentorship to help advertisers optimize their targeting, bids, budgets, inventory, and business goals through advanced machine learning techniques, including GenAI. This high-impact, fast-growing area demands the use of massive datasets and modern ML methods across ranking, forecasting, optimization, and experimentation.

As a Machine Learning Engineer within our Advertising team, you will contribute significantly to designing machine learning models and algorithms, directly affecting our advertising systems.

What you will accomplish:

• Build Production ML Systems: Design, develop, and deploy scalable machine learning models and services for ad ranking, recommendation, and advertiser optimization.

• Drive Data-Informed Decisions: Analyze large-scale production data to uncover insights, define problem spaces, and identify high-impact ML opportunities.

• Own the ML Lifecycle: From feature engineering and model training to evaluation, deployment, and monitoring in production environments.

• Collaborate multi-functionally: Partner with product managers, applied researchers, and engineers to translate business goals into solutions powered by machine learning.