Applied Researcher 2, Query Science

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 Query Science team is at the core of eBay’s Search product and it is composed of passionate professionals united in our mission to make searching and buying at eBay as efficient and enjoyable as possible. Innovation is at the heart of everything we do. We believe that by understanding the intent and behaviors of our buyers, we can tailor our services to provide the best possible shopping experience, setting new standards in the e-commerce industry. Join us, and be part of a forward-thinking company that values creativity, hard work, and innovation. What Will You Do? Looking to make an impact on the future of global commerce? Do you want to shape how millions of people buy, sell, and engage around the world? ​The Search Query Science team is the biggest contributor to eBay’s search/query processing and drives a significant portion of revenue. We are growing at a rapid pace and committed to building a stellar team. We are a team where people who think and do things differently. Our team is results-oriented and hardworking. We are building solutions for core e-commerce search problems such as query-to-item embedding based retrieval, item-to-item embedding based retrieval, search relevance model, query recovery with state-of-the-art ML algorithms tailored to understand large-scale user behavioral signals. The environment is friendly and fun. We get things done that make a difference. ​We are looking for stellar applied researchers to join us and build the next generation of query science products in eBay Search. If you enjoy the scale and technical complexity of query processing and want to be at the frontier of applied research in e-commerce, join now. Help us redefine query understanding at eBay. ​Job Responsibilities ​Research, develop, and productionize large-scale retrieval algorithms for eBay Search, with a primary focus on query-to-item embedding-based retrieval and item-to-item retrieval. 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. Understand and contribute to the end-to-end search stack, including query understanding, candidate retrieval, ranking, and relevance evaluation. Collaborate with relevance and ranking teams on model integration and optimization, with opportunities to contribute directly to search relevance models. Design rigorous offline and online experiments, define appropriate evaluation metrics, and analyze model performance, failure cases, trade-offs, and business impact. Translate state-of-the-art research in information retrieval, NLP, representation learning, and large language models into practical, scalable solutions for real-world