Senior Applied Researcher

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:

We are the Focus Category Applied Science team — a group of applied scientists and engineers innovating at the intersection of AI and ecommerce. Our mission is to personalize and optimize the buyer journey across eBay’s most strategic verticals: Electronics, Parts & Accessories (P&A), Fashion, and Vehicles.

P&A is the largest category on eBay and a critical growth engine for the company. We focus on helping buyers discover the right inventory faster, smarter, and more contextually through applied machine learning.

We are working on challenges such as:

• Building recommender systems that surface the most relevant listings based on user behavior, context, and item signals.
• Developing personalized banners, dynamic landing pages, and real-time ranking models for buyer experiences.
• Integrating structured data, text, and images to deliver rich, context-aware personalization.
• Running large-scale experiments to measure and optimize conversion, engagement, and satisfaction.

We’re passionate about applying science to real-world problems at massive scale—and we’re looking for new teammates who are excited to make an impact.

What you will accomplish:

• Design, develop and productionize machine learning models for personalization, recommendation, and ranking.
• Build scalable systems that deliver real-time buyer experiences across millions of users and inventory listings.
• Conduct A/B experiments and use data-driven insights to iterate and optimize models.
• Work closely with product managers, engineers, and designers to integrate science-driven features into the buyer experience.
• Document and communicate technical approaches, insights, and results to technical and business audiences.
• Contribute to the science strategy by identifying new opportunities and approaches based on data trends and business goals.

What you will bring:

• 5+ years of industry experience applying machine learning at scale, ideally in recommendation, ranking, or personalization domains.
• MS or PhD in Computer Science, Machine Learning, Statistics, or a related technical field.
• Strong programming skills in Python and experience with ML frameworks such as PyTorch or TensorFlow.
• Experience working with large datasets and distributed computing frameworks like Spark or Hive.
• Strong background in experimental design, A/B testing, and statistical analysis.

Nice to Have:

• Experience in ecommerce, online marketplaces, or consumer-facing applications.
• Knowledge of multimodal machine learning techniques (e.g., using structured data, text, and image signals together).
• Familiarity with recommender system architectures, ranking algorithms, or retrieval systems.
• Experience building models that prioritize privacy, fairness, and user trust.