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 team and the role:

Our Recommendations team works on delivering item recommendations at scale and in near real time to our buyers on our website and native app platforms. Recommendations are a core part of how our buyers navigate eBay’s vast and varied inventory. Our team develops state-of-the-art recommendations systems, including deep learning based retrieval systems for personalized recommendations, machine learning ranking models, as well as advanced MLOps in a high volume traffic industrial e-commerce setting.

We are exploring a new generation of recommender systems powered by Large Language Models and other emerging paradigms in AI. We are looking for an Applied Scientist and innovator who can drive state of the art ML modeling and get systems into production at eBay scale. 

What you will accomplish:

• Build data pipelines needed to support user input, recall and ranking, model training and inference, working with architects and other engineering peers.
• Track the fast paced world of AI and apply innovative techniques to unique and large data sets of unstructured multimodal data representing eBay's vast and varied inventory, and millions of users.
• Develop and deploy innovative models in the context of recommender systems.
• Deploy big data technology and large scale data pipelines to create magical AI applications and user experiences.
• Have a real impact on how shoppers interact with one of the world's largest e-commerce marketplaces to find what they love every day.
• Drive marketplace GMB as well as advertising revenue via organic and sponsored recommendations.

What you will bring:

• MS in Computer Science or related area with 3 years of relevant work experience (or BS/BA with 5 years) in ML / AI / Data Engineering
• Experience in production engineering practices and software development in an OO language (Scala, Java, etc.) 
• Experience in AI applied research, preferably in Natural Language Processing (NLP), deep learning, and industrial recommender systems
• Experience in big data processing and analysis, e.g. Hadoop, SQL, Spark, Pyspark is required
• Experience with using cloud services, big data pipelines and databases

Additional Details

The base pay range for this position is expected in the range below:

Base pay offered may vary depending on multiple individualized factors, including location, skills, and experience. The total compensation package for this position may also include other elements, including a target bonus and restricted stock units (as applicable) in addition to a full range of medical, financial, and/or other benefits (including RRSP eligibility, various paid time off benefits, such as PTO and parental leave). Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

This job posting relates to an existing vacancy within eBay.

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, 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.

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