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, NLP, 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 machine learning, deep learning, NLP, information retrieval, or recommendation systems.
• Hands-on experience with representation learning, embedding models, semantic retrieval, or neural ranking, preferably applied to search, recommendation, advertising, or other large-scale retrieval problems.
• Solid understanding of modern dense retrieval methods, such as dual-encoder architectures, contrastive learning, negative sampling, and approximate nearest-neighbor search.
• Familiarity with the end-to-end search pipeline, including query understanding, candidate generation, retrieval, ranking, and relevance evaluation.
• Experience developing and evaluating machine-learning models using large-scale, real-world datasets, including the ability to conduct rigorous offline experiments and interpret online A/B testing results.
• Strong programming skills in Python and proficiency in at least one additional language, such as Java, Scala, or C++.
• Experience with modern coding agents such as Claude Code, Codex, or Cursor.
• Familiarity with large-scale data processing and distributed-computing technologies, such as SQL, Spark, Hadoop, or equivalent systems.
• Strong analytical and problem-solving skills, with the ability to transform ambiguous product or business challenges into well-defined scientific problems.
• Excellent written and verbal communication skills in English, with the ability to collaborate effectively across research, engineering, and product teams.

Additional Details

eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at [email protected]. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities.

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