Applied Researcher 2, Recommendation

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. eBay Live is a rapidly growing strategic business that brings buyers, sellers, and communities together through interactive live-shopping experiences. By combining engaging live content with eBay’s unique inventory and marketplace, eBay Live is creating new ways for buyers to discover products, connect with sellers, and participate in commerce. The Recommendations Science team is a core organization powering personalized content and product distribution for eBay Live. We develop recommendation algorithms that connect each user with the most relevant 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 systems for a dynamic, interactive, and fast-growing live-commerce platform? Do you want your work to directly shape how users discover and engage with live-shopping content? We are looking for an Applied Researcher to develop the next generation of recommendation technologies for eBay Live. You will work across the recommendation stack, from understanding user interests and generating candidates to ranking live events and optimizing personalized distribution. Live recommendation 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 PhD in Computer Science, Artificial Intelligence, Machine Learning, Recommender Systems, Information Retrieval, or a related field with 3-5 years of relevant industry experience; or a Master’s degree with 5-7 years of relevant industry experience. Strong foundation in recommender systems and hands-on experience with multiple stages of the recommendation pipeline, such as user understanding, retrieval, ranking, re-ranking, personalization, and evaluation. Experience with modern recommendation techniques, such as embedding-based retrieval, collaborative filtering, deep ranking, sequential recommendation, multi-task learning, or representation learning. Proven ability to develop and evaluate machine learning models using large-scale, real-world behavioral data and translate research ideas into practical, scalable solutions. Experience designing rigorous offline evaluations and online experiments, with an understanding of recommendation metrics, user engagement, and business impact. Strong programming skills in Python and proficiency in at least one of Java, Scala, or C++,