Software Engineer, ML Systems

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.

Looking for a company that inspires passion, courage and creativity, where you can be on the team shaping the future of global commerce? Want to shape how millions of people buy, sell, connect, and share around the world? If you’re interested in joining a purpose driven community that is dedicated to crafting an ambitious and inclusive work environment, join eBay – a company you can be proud to be with.

Our Recommendations team works on delivering 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 learned ranking models, GenAI/LLM powered recommendations, as well as advanced MLOps in a high volume traffic industrial e-commerce setting.

We are building cutting edge recommender systems powered by the latest ML, NLP, LLM/GenAI/RAG and AI technologies. Additionally, we are building production integrations with Google GCP Vertex AI platforms to supercharge our item recommendation algorithms. Come join our innovative engineering and applied research team!

This is an opportunity to:

• Influence how people will interact with eBay’s recommender systems in the future, and how recommender systems technology will evolve

• Work with unique and large data sets of unstructured multimodal data representing eBay's vast and varied inventory, including billions of items and millions of users

• Develop and deploy state-of-the-art AI models to production which have direct measurable impact on eBay buyers

• Deploy big data technology and large scale data pipelines

• Drive marketplace GMB as well as advertising revenue via organic and sponsored recommendations

Qualifications:

• MS in Computer Science or related area with 3 years of relevant work experience (or BS/BA with 5 years) in Engineering / Machine Learning / AI

• Experience building large scale distributed applications and expertise in any OO language (Scala, Java, etc.) 

• Experience building with no sql databases and key value stores (MongoDB, Redis, etc)

• Generalist with a can do attitude and willingness to learn/pick up new skill sets as needed

• Experience with big data pipelines (Hadoop, Spark) is a plus 

• Experience in AI applied research and industrial recommendation systems is a plus

• Experience with Large Language Models (LLMs) and prompt engineering is a plus

Links to some of our previous work:

• Tech Blog 2025 (Multimodal GenAI)

• Tech Blog 2025 (GenAI Agentic Platform)

• RecSys 2024 Workshop paper

• Google Cloud Blog 2024

• eBay Tech Blog 2023

• eBay Tech Blog 2022

• RecSys 2021 paper

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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 talent@ebay.com. 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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