Applied Researcher 2

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. Who Are We? eBay Inc. is a global commerce leader that connects millions of buyers and sellers around the world. We exist to enable economic opportunity for individuals, entrepreneurs, businesses, and organizations of all sizes. The Recommendations Science team is a core organization powering personalized content and product distribution for eBay. We develop recommendation algorithms that connect each user with the most relevant listings, live events, sellers, and items at the right moment. Our work spans the end-to-end recommendation ecosystem, including candidate generation, retrieval, ranking, personalization, real-time user modeling, and recommendation evaluation. Join us and help build the intelligence behind one of eBay’s most exciting and rapidly evolving customer experiences. What Will You Do? Are you excited about building recommendation & ads systems for a dynamic, interactive, and fast-growing e-commerce platform? Do you want your work to directly shape how users discover and engage with shopping content? We are looking for an Applied Researcher to develop the next generation of recommendation & ads technologies for eBay. You will work across the recommendation & ads stack, from understanding user interests and generating candidates to ranking listings and optimizing personalized distribution. eBay recommendation & ads presents unique scientific challenges. Available content changes continuously, user interests evolve in real time, and signals such as viewing, clicking, commenting, bidding, and purchasing may reflect different levels of intent. In this role, you will apply state-of-the-art machine learning and recommender-system techniques to address challenges including real-time personalization, content freshness, cold start, sparse feedback, diversity, and multi-objective optimization. ​Job Responsibilities • ​Research, develop, and productionize large-scale retrieval algorithms for eBay Recommendation & Ads System. • Build and optimize dense retrieval systems, including representation learning, training-data construction, negative sampling, model training, approximate nearest-neighbor search, and online serving. • Explore sparse and hybrid retrieval approaches and effectively combine lexical and semantic signals to improve search relevance, recall, and coverage. • Develop models that learn from large-scale behavioral and catalog data to better represent user queries, items, and shopping intent. • Design rigorous offline and online experiments, define appropriate evaluation metrics, and analyze model performance, failure cases, trade-offs, and business impact. • Work closely with engineering and product teams to deploy models into production and continuously improve their quality, efficiency, scalability, and reliability. • Promote sound scientific methodologies and engineering best practices across teams, and present impactful technical or research work internally and at external conferences or industry forums. Requirements • PhD in Computer Science, Artificial Intelligence, Information Retrieval, Machine Learning,