MTS-2 Machine Learning Engineer

About the role

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

eBay’s 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 Machine Learning Engineer, 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 cross-org 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 candidate’s 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.

• Champion software engineering best practices, including code reviews, testing, and documentation, within the machine learning team.

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

• Mentor other team members through code reviews, technical guidance, architecture design, and pair programming.

What you will bring

• MS in Computer Science or related area with 7+ years of relevant work experience (or BS/BA with 9+ years) in in ML / AI / Data Engineering.

• Expert 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. Experience with serving frameworks (TensorFlow Serving, TorchServe, NVIDIA Triton) and libraries for LLM operations (LangChain, Hugging Face Transformers) preferred.

• 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.

• A masterful understanding of the challenges and requirements of running machine learning in a live, 24/7 production environment, including monitoring, alerting, and incident response.

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 eBay’s Multi-Arm Bandits Elevate Product Recommendations Through Dynamic Pagination

• Improving eBay’s Fashion Fitment Using Size Signals From Search Queries

Additional Details

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