Staff ML Engineer - Advertising

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About the role

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 • Lead End-to-End ML Systems at Scale : Architect, build, and evolve large-scale machine learning systems powering ad ranking, recommendations, and advertiser optimization, with ownership over system design, reliability, and long-term technical direction. • Drive Strategic, Data-Informed Decisions : Leverage large-scale production data to identify high-impact opportunities, define ambiguous problem spaces, and influence product and business strategy through data-driven insights. • Own and Elevate the ML Lifecycle : Set best practices across the full ML lifecycle—feature engineering, model development, evaluation, deployment, and monitoring—while improving robustness, reproducibility, and scalability of production pipelines. • Collaborate Across Functions to Shape Solutions : Partner closely with product, engineering, and research to translate complex business objectives into scalable ML solutions, influencing roadmap and prioritization through technical expertise. • Optimize System Performance at Scale : Define, own, and evolve key system metrics (e.g., relevance, revenue, latency, reliability). Lead efforts to improve system efficiency, scalability, and cost-performance tradeoffs across high-throughput environments. • Advance ML Innovation in Production : Drive adoption of state-of-the-art approaches (e.g., deep learning, GenAI, LLM-based systems) and translate modern technology into practical, high-impact production systems. • Provide Technical Leadership and Mentorship : Mentor engineers, lead design reviews, and set engineering standards. Act as a technical anchor for the team, setting a higher standard on ML engineering excellence and experimentation rigor. What You Will Bring • Master’s or PhD in Computer Sc

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