Lead the development, governance, and operationalization of item policies across eBay Inc., including global marketplace, emerging commerce experiences, and subsidiaries.
Compensation
$166,000 - $221,600
Responsibilities
Own global item policy strategy across Prohibited & Restricted Items, Product Safety, Intellectual Property, and Counterfeit & Authenticity
Translate emerging legal, regulatory, market, and brand risks into clear policy positions, requirements, and enforcement recommendations
Establish risk-based prioritization frameworks that direct organizational resources toward the highest-impact compliance and marketplace integrity risks
Lead complex policy decisions and tradeoffs, balancing regulatory obligations, buyer and seller protection, enforcement feasibility, and commercial impact
Ensure consistent policy principles across geographies, categories, and eBay Inc. businesses, while accounting for differences in local regulations and marketplace risk
Requirements
Nice to have: AI/ML-driven detection
Nice to have: Policy taxonomies
Nice to have: Machine-readable policy
Nice to have: Human-in-the-loop decisioning
Nice to have: International regulatory environments
Design, develop, and apply machine learning solutions to real‑world personalization, product, and business problems, translating ambiguous opportunities into scalable models, experiments, and production‑ready capabilities.
Locations (2)
San Jose, California, United States
Seattle, Washington, United States
Compensation
The total cash range for this position in San Jose is $187,000.00 to $261,500.00. Employees in this role have the potential to increase their pay up to $299,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.
Responsibilities
Design, develop, and apply machine learning solutions to real‑world personalization, product, and business problems
Drive end‑to‑end scientific work across problem formulation, data exploration, feature engineering, model development, evaluation, and iteration
Partner closely with engineers, product, and business stakeholders to integrate machine learning solutions into services and workflows
Use strong technical judgment to select appropriate methods, validate outcomes, and improve model performance, reliability, and operational quality
Design, develop, and evolve enterprise-scale AI and Knowledge Engineering architectures, frameworks, and technical standards.
Define reference architectures and implementation patterns for LLMs, RAG, Knowledge Graphs, semantic technologies, and knowledge-centric solutions.
Design reusable architectural components that enable semantic reasoning, intelligent search, knowledge retrieval, and Generative AI applications.
Architect scalable foundations for secure, governed, reliable, and enterprise-ready AI solutions.
Develop strategic roadmaps for the adoption and operationalization of AI platforms, knowledge graphs, semantic layers, and enterprise knowledge ecosystems.
Requirements
Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field
Nice to have: Knowledge Graphs
Nice to have: semantic technologies
Nice to have: Ontology Design
Nice to have: Retrieval-Augmented Generation (RAG)
Design, build, integrate, deploy, and optimize AI-enabled solutions, intelligent automation, and agentic systems across Financial Shared Services.
Compensation
€68,300 - €113,800
Responsibilities
Design and develop AI-enabled applications, intelligent automation workflows, and agentic systems supporting FSS operations and decision-making.
Translate business and process requirements into scalable technical architectures, solution designs, engineering plans, and production-ready capabilities.
Establish reusable design patterns, frameworks, components, coding practices, and technical standards for agent and automation development.
Build, configure, test, deploy, and enhance AI agents supporting workflows, operational processes, knowledge retrieval, intelligent routing, anomaly detection, and decision support.
Develop APIs, integrations, data pipelines, and orchestration capabilities connecting agents with enterprise systems, data sources, and workflow platforms.
Requirements
Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, Artificial Intelligence, Engineering, or a related technical discipline
Nice to have: Master's degree in a related technical field
Nice to have: Microsoft Azure AI
Nice to have: Azure OpenAI
Nice to have: Microsoft Copilot Studio
Safely integrate and operate AI/ML‑enabled solutions that improve outcomes
Requirements
Bachelor's degree in Computer Science or a related technical field; or Equivalent related professional experience
Nice to have: advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field