Applied Researcher Agentic Systems

The Agentic Systems Research team is building the next generation of intelligent systems that assist buyers and sellers across eBay’s global marketplace. Our mission is to develop AI agents capable of reasoning, planning, and acting across complex commerce environments, helping users discover products, make decisions, manage inventory, and complete transactions more effectively.

As an Applied Researcher, you will work at the intersection of large language models, retrieval systems, recommender systems, and agent architectures to develop AI systems that can operate reliably in real-world commerce workflows. You will collaborate with engineers, product leaders, and other researchers to transform research ideas into production systems used by millions of customers.

This role combines applied research, experimentation, and system development to advance the science and practice of agentic AI in large-scale marketplaces.

What you will accomplish:

• Design and prototype agentic AI systems capable of multi-step reasoning, planning, and tool use across eBay’s marketplace infrastructure.

• Develop and evaluate LLM-based systems for commerce tasks such as product discovery, listing creation, pricing guidance, customer support, and seller assistance.

• Advance retrieval-augmented generation (RAG) and knowledge-grounded AI techniques for reasoning over structured marketplace data including catalog information, listings, and seller tools.

• Build robust evaluation frameworks for agentic systems, including offline evaluation, synthetic task generation, human evaluation, and online experimentation.

• Collaborate closely with engineering teams to translate research prototypes into reliable production systems operating at large scale.

• Analyze large-scale datasets to understand user behavior and improve AI-driven relevance, personalization, and decision support.

• Contribute to the broader AI research community through technical publications, patents, and conference presentations where appropriate.

What you will bring:

Education:

• Ph.D. or M.S. in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related technical field.

Technical Expertise: 

• Strong understanding of modern machine learning and deep learning techniques, particularly in areas such as: Large Language Models (LLMs), Natural Language Processing, Recommender systems and personalization, Information retrieval and search, Retrieval-augmented generation (RAG) etc.

• Experience working with large-scale datasets and distributed data processing systems.

• Strong programming skills in Python and modern ML frameworks, with experience working with large-scale data platforms such as Spark or similar distributed systems.

Research & Problem Solving

• Experience designing and running machine learning experiments, including hypothesis development, ablation studies, error analysis, and model iteration.

• Familiarity with evaluation methodologies such as offline metrics, human evaluation, and A/B testing.

• Strong analytical skills and ability to derive insights from large-scale behavioral data.

Collaboration & Communication

• Excellent written and verbal communication skills with the ability to explain complex technical concepts clearly.

• Ability to work effectively in cross-functional teams spanning research, engineering, and product.