Job Description

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 Search & Recommendation team is at the core of eBay’s discovery experience. We build large-scale machine learning systems that help buyers find relevant products, discover new interests, and connect with the right inventory and content. Our work spans the end-to-end search and recommendation ecosystem, including query understanding, candidate retrieval, ranking, personalization, and real-time inference.

We operate at eBay’s global scale, where model quality must be balanced with demanding requirements for latency, throughput, reliability, and infrastructure efficiency. Our Machine Learning Engineers work closely with Applied Researchers and platform engineers to transform cutting-edge algorithms into scalable and reliable production systems.

Join us and help build the machine learning infrastructure and applications that power the next generation of search and recommendation experiences at eBay.

What Will You Do?

Are you excited about building high-performance machine learning systems that serve millions of users? Do you enjoy working at the intersection of machine learning, distributed systems, and performance optimization?

We are looking for an experienced Machine Learning Engineer to develop and optimize production systems across eBay’s search and recommendation stack. You will partner closely with Applied Researchers to turn advanced retrieval, ranking, and personalization models into highly efficient production solutions.

Your work may include building large-scale embedding-based retrieval systems, optimizing GPU inference for ranking models, developing efficient model-serving architectures, and improving the reliability and velocity of model deployment. You will solve challenging engineering problems involving billions of items and behavioral signals, strict latency requirements, high request volumes, and rapidly evolving machine learning architectures.

As a senior individual contributor, you will independently lead complex engineering projects, influence technical architecture, and establish best practices for production machine learning across search and recommendation.

​Job Responsibilities

• Design, build, and operate large-scale machine learning systems across the search and recommendation stack, including candidate retrieval, ranking, personalization, and real-time inference.
• Partner closely with Applied Researchers to productionize state-of-the-art algorithms, translating research prototypes into scalable, reliable, and maintainable systems.
• Build and optimize large-scale retrieval systems, including embedding generation, vector indexing, approximate nearest-neighbor search, distributed candidate retrieval, and online model serving.
• Optimize inference for complex retrieval and ranking models on GPUs and other accelerators, improving latency, throughput, memory utilization, and infrastructure cost.
• Develop high-performance serving architectures for deep learning models, including batching, caching, model compression, quantization, compilation, and distributed inference.
• Build reusable machine learning platforms, tools, and pipelines for training, evaluation, deployment, monitoring, experimentation, and model lifecycle management.
• Improve the availability, observability, and operational excellence of production machine learning systems through monitoring, profiling, automated testing, and failure diagnosis.
• Collaborate across science, engineering, and product teams to define technical roadmaps and deliver measurable improvements in relevance, engagement, system performance, and business impact.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Machine Learning, or a related technical field, with substantial experience building production machine learning or large-scale distributed systems with 7-10 years of relevant industry experience..
• Strong software engineering skills and proficiency in one or more languages such as Python, Java, Scala, C++, or CUDA, with a track record of designing reliable, maintainable, and high-performance systems.
• Experience building production systems for search, recommendation, advertising, or other large-scale machine learning applications, with familiarity across retrieval, ranking, personalization, or model serving.
• Hands-on experience with large-scale retrieval technologies, such as embedding-based retrieval, vector indexing, approximate nearest-neighbor search, distributed search, or sparse and dense retrieval.
• Experience optimizing deep learning inference on GPUs or other accelerators, including profiling, batching, quantization, compilation, memory optimization, or distributed inference.
• Proficiency with machine learning frameworks and serving technologies such as PyTorch, TensorFlow, ONNX, TensorRT, Triton, or equivalent systems, as well as large-scale data-processing technologies such as Spark, Hadoop, or SQL.
• Strong understanding of distributed systems and production engineering, including scalability, concurrency, latency optimization, observability, fault tolerance, capacity planning, and cloud or containerized infrastructure.
• Demonstrated ability to lead complex cross-functional projects, collaborate effectively with Applied Researchers and product engineers, and translate emerging machine learning research into reliable production solutions.

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 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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