Staff Software Engineer, Search Ads

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
The Search Ads team is one of the largest contributors to eBay's advertising business and a significant driver of company revenue. We build the promoted-listings products — cost-per-click and cost-per-action — that appear in eBay search, and the machinery behind them: candidate retrieval, multi-stage ranking, relevance and conversion models, auction mechanics and autobidding.
The team is a blend of applied researchers and engineers working as one unit. Researchers develop the models; engineers make them real at scale — and increasingly the line between the two is where the most interesting work happens. We run on GPU-based training pipelines and large-scale feature and data systems that power every ad decision in eBay search.
As a Staff Software Engineer you will be a technical leader on the engineering side of this stack. You will set standards, own critical systems end to end, and act as the bridge that turns research breakthroughs into production impact. We are growing quickly and reward people who take ownership, do things differently, and deliver.

What you will accomplish

• Own the technical vision for search monetization engineering. Define and raise the engineering standards, architecture and processes for the systems that serve ads in eBay search, with direct impact on the buying experience and on revenue.
• Take advanced ML models to production. Partner with applied researchers to deploy complex, scalable models — deep ranking and relevance models, retrieval models, autobidding and pricing logic — and build the engineering path that makes deployment safe, fast and repeatable.
• Build the data and training backbone. Design and operate robust large-scale data pipelines, feature stores and GPU-based training workflows that researchers rely on daily.
• Build real-time monitoring and optimization systems. Create the algorithms and tooling that watch live auction and ranking behavior, catch regressions early, and tune the system in real time.
• Lead across the team. Drive design reviews, mentor engineers, and set a higher standard on code quality, reliability and operational excellence. Be the engineer researchers want to work with, and the one who pushes back when a design will not survive production.

What you will bring

• 4+ years of software design and development experience, with a strong computer-science foundation: data structures, algorithms, object-oriented and functional design, and system design at scale.
• 3+ years building large-scale backend systems and data-intensive applications in Java, C++, Scala, Python or similar.
• Hands-on experience designing and operating big-data processing pipelines (Spark, Hadoop, Hive, ETL frameworks) in production.
• Experience taking machine learning models to production: feature pipelines, online/offline consistency, validation and rollout, and production monitoring.
• A track record of technical leadership: owning systems end to end, driving architecture decisions, and mentoring other engineers.
• Bachelor's degree in computer science or engineering (or equivalent professional experience) with 5+ years of experience, or a Master's degree in computer science or engineering (or equivalent professional experience) with 3+ years of experience.

Nice to have

• Experience in search, recommendations or online advertising systems — ranking, retrieval, auctions or bidding.
• Familiarity with deep learning frameworks (PyTorch, TensorFlow) and GPU training infrastructure.
• Experience with MLOps and ML platform tooling (for example Kubeflow, MLflow, Ray or in-house equivalents).
• Background in experimentation platforms and large-scale A/B testing.

Why you'll love it here

• Scale that matters. Your work touches every search on eBay, and you can measure its effect on revenue in days.
• Research in the room. You will work directly with applied researchers on state-of-the-art ranking, retrieval and autobidding — and have a real say in how their models are built, not just where they run.
• Modern stack, real ownership. GPU training pipelines, large-scale data systems and ML deployment tooling.
• A team that rewards initiative. People who do things differently and win are recognized and grow here.

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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