Software Engineer, MTS 1

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 fosters passion, courage, and creativity, where you can join the team crafting the future of global commerce? Want to build how millions of people buy, sell, connect, and share around the world? If you want to be part of a purposeful community focused on developing an ambitious and inclusive work environment, join eBay – a company you can be proud to be with.

Our Search & Recommendations team delivers near real-time, highly scaled recommendations to buyers across our website and mobile apps. These recommendations are essential for helping users discover eBay’s vast and diverse inventory. We build the large-scale search, recommendation, and personalization systems that drive the core e-commerce experience at eBay.

We are currently developing innovative ranking and recommender systems utilizing the latest advancements in ML, NLP, LLMs, and AI. We are seeking a Software Engineer to help design, implement, test, and deploy highly reliable recommendation features at eBay scale. You will collaborate with product managers, engineers, and applied research leaders across our global Search & Recommendations group to build, launch, monitor, and continuously improve personalized shopping experiences.

What you will accomplish:

• Drive engineering delivery of high-traffic search and recommendation experiences at eBay.
• Develop and maintain large-scale production services, feature pipelines, model integration points, and experimentation workflows that support real-time and near real-time recommendations.
• Work with large-scale distributed systems handling high-volume traffic, distributed data stores, billions of daily impressions, and strict low-latency SLA requirements.
• Work alongside a team of applied researchers and engineers with deep expertise in recommender systems, machine-learned ranking, natural language processing, computer vision, AI/LLMs, and ML production engineering.
• Collaborate with product, analytics, applied research, and engineering teams to transform feature requests into build options, implementation plans, production launches, and measurable business outcomes.
• Support iterative A/B testing by enabling experiments, analyzing results, learning from outcomes, and applying those findings to drive the next feature iteration.

What you will bring:

• A Bachelor’s degree in Computer Science or a related field with over five years of relevant experience in Software Engineering, or a Master’s degree with over four years of relevant experience.
• Expertise with object-oriented languages such as Java, Scala, or similar production languages in a high-scale distributed setting, building with SOLID principles.
• Experience working with large-scale real-time systems, data pipelines, streaming or batch processing, and distributed data platforms like Hadoop, Kafka, Spark, Flink, or comparable technologies is advantageous.
• Experience with Python, ML pipelines, feature engineering, model integration, model evaluation, or timely LLM workflows is a plus.
• Demonstrated ability to work independently on prioritized functional areas while communicating assumptions, risks, task clarifications, and tradeoffs to collaborators upfront.
• Experience translating product or research requirements into technical build options, estimates, feature specs, and production-ready implementation plans.
• Experience working with search, recommendations, personalization, experimentation, or machine learning systems is strongly preferred.
• Experience in A/B testing, experiment configuration, launch evaluation, and data-guided product iteration is a plus.
• Previous experience publishing academic papers, patents/IP, or technical blogs is a plus.

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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