SWE-3 Backend Engineer, ML Systems

At eBay were more than a global ecommerce leader were changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. Were 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. Were 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:

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 youre 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.

eBays Ads teams build the production systems that help buyers discover relevant inventory and help sellers grow their businesses. These systems span multiple applied ML domains including sponsored search item recommendations seller guidance experiences ranking retrieval personalization and GenAI-powered capabilities.

As a Backend Engineer ML Systems you will help build the platforms services APIs data pipelines and MLOps capabilities that bring cutting edge AI models into production at eBay scale. You will engineer the data pipelines that feed our models build the infrastructure to train and serve them and develop the APIs that deliver personalized experiences to millions of users. You will work at the intersection of machine learning and software engineering solving complex challenges in system design and the operationalization of the latest Generative AI technologies. You will partner closely with Applied Researchers Product Managers and Engineering teams to turn prototypes into robust scalable low-latency systems for millions of customers.

This is a shared hiring role across Ads teams. Final team placement will be determined after offers based on each candidates experience strengths and fit with the needs of Ads Recommendations Ads Search and Ads Guidance.

What you will accomplish:

• Design build and operate scalable backend and ML systems supporting sponsored experiences ranking retrieval and personalization.

• Develop and own the MLOps pipelines for continuous integration continuous delivery (CI/CD) training validation and monitoring of all production-grade models.

• Engineer robust data pipelines using big data technologies to process vast datasets for model training and feature engineering.

• Implement and optimize both traditional ML models and state-of-the-art Generative AI models (including LLMs) for low-latency serving and high-throughput environments.

• Collaborate closely with Applied Researchers to translate novel algorithms and research prototypes into hardened production-ready code.

• Monitor system performance identify and resolve production issues and continuously improve the reliability and efficiency of our ML services.

What you will bring:

• BS in Computer Science or related area with 4 years of relevant work experience in ML / AI / Data Engineering.

• Experience in production engineering practices and software development in an OO language (Scala Java Python etc.).

• Extensive experience in big data distributed processing frameworks e.g. Apache Hadoop Spark Flink.

• Experience with ML frameworks like TensorFlow and PyTorch from a production perspective.

• Proven ability to build and manage CI/CD pipelines for ML models including proficiency with containerization (Docker Kubernetes).

• Experience with using cloud services big data pipelines and databases e.g. AWS GCP Azure.

• Proven ability to design and build scalable distributed systems and expose their functionality through well-designed RESTful or gRPC APIs.

Links to some of our previous work:

• How eBay Created a Language Model With Three Billion Item Titles

• Complementary Item Recommendations at eBay Scale

• Transforming Fashion Discovery in E-Commerce through Theme-Based Categorization with GenAI

• Scroll into the Future: How eBays Multi-Arm Bandits Elevate Product Recommendations Through Dynamic Pagination

• Improving eBays Fashion Fitment Using Size Signals From Search Queries

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 you have a need that requires accommodation please contact us at. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBays commitment to ensuring digital accessibility for people with disabilities.

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