Staff AI/ML Engineer - MTS2

Ebay Compliance Engineering Staff AI/ML Engineer

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 Role

At eBay, Compliance Engineering builds systems and technical controls. These ensure our global marketplace operates flawlessly and follows evolving regulatory requirements. Our mission is to turn complex legal, policy, and regulatory obligations into scalable, reliable, and auditable engineering solutions. These solutions protect the platform's integrity and maintain trust for millions of buyers and sellers worldwide.

We are seeking a Staff AI/ML Engineer (MTS2) to serve as a senior technical contributor within the Compliance Engineering organization. This role focuses on crafting and delivering high-scale, production-grade AI systems for compliance use cases, including classification, policy enforcement, monitoring, and auditability.

You will work extensively with Generative AI, Retrieval-Augmented Generation (RAG), advanced prompt and context engineering, and agentic AI systems to build reliable, explainable, and regulation-ready solutions. This is a systems role passionate about correctness, control, and operating AI in high-risk environments.

Primary Job Responsibilities

• Provide technical leadership across machine learning and software initiatives, influencing system build, architecture, and long-term technical direction.
• Define and drive technical strategy for AI-powered compliance systems, aligning solutions with regulatory requirements, product goals, and platform constraints.
• Build and deliver large-scale ML and AI systems supporting compliance classification, policy enforcement, monitoring controls, and audit automation.
• Architect and implement end-to-end AI systems, from data pipelines and feature engineering through model training, LLM integration, deployment, and real-time or batch inference.
• Develop and enhance Generative AI systems, including Retrieval-Augmented Generation (RAG) pipelines over regulatory and policy corpora, hybrid retrieval systems, query rewriting, retrieval orchestration, and grounding mechanisms.
• Apply advanced timely engineering and context engineering techniques, including structured timely inputs, tool-augmented workflows, controlled outputs, schema enforcement, and function calling.
• Build and implement agentic AI systems for compliance workflows. These include tool-integrated agents working with internal systems and knowledge bases, multi-step reasoning and planning workflows, and multi-agent systems with specific roles.
• Lead complex, multi-quarter technical initiatives, using system and business metrics to drive decisions and communicate tradeoffs clearly.
• Own and evolve system architecture, proactively addressing technical debt while ensuring scalability, reliability, and alignment with compliance and product requirements.
• Develop and uphold thorough assessment frameworks for Generative AI and agent systems, incorporating groundedness and factuality checks, hallucination identification and reduction, and regression testing to ensure system reliability.
• Review, validate, and audit AI system behavior and outputs, ensuring correctness, security, and compliance with regulatory standards.
• Translate ambiguous legal, regulatory, and policy requirements into scalable, explainable, and auditable system builds.
• Establish and monitor key system and model metrics to assess performance, reliability, and compliance readiness.
• Lead complex debugging and system analysis across distributed and data-powered systems, resolving ambiguous failures and reliability issues.
• Collaborate closely with Legal, Policy, Risk, Product, and Engineering teams to deliver compliant, production-ready solutions.
• Multiply team impact through mentorship, build reviews, and raising engineering standards across AI system development.

Required Skills and Experience

• MS or PhD in Computer Science, Statistics, Mathematics, or a related field, or equivalent practical experience.
• 8+ years of experience in software engineering and machine learning, with strong fundamentals in data structures, algorithms, and system development.
• Proven experience leading technical initiatives and influencing engineering direction without direct people management responsibility.
• Demonstrated success building, deploying, and operating production-grade ML systems and services at scale.
• Strong hands-on expertise in Machine Learning, NLP, and Generative AI, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, timely engineering, and context engineering.
• Practical experience developing agentic AI systems, such as tool-using agents, multi-step reasoning pipelines, agent orchestration frameworks, and multi-agent coordination patterns.
• Solid grasp of reliability issues in Generative AI and agent systems, such as addressing hallucinations, balancing deterministic and non-deterministic factors, managing errors, retries, and fallback strategies.
• Experience with vector databases and retrieval systems (e.g., FAISS, Milvus, Pinecone, OpenSearch vector, or similar).
• Experience implementing guardrails, validation layers, and policy enforcement mechanisms for AI systems.
• Strong expertise in distributed systems and large-scale data processing (e.g., Spark, streaming systems).
• Experience crafting and managing evaluation frameworks, testing strategies, and monitoring systems for ML and AI systems.
• Proficiency in Python and at least one additional programming language such as Java or Scala.
• Strong analytical and problem-solving skills, with the ability to navigate ambiguity and drive clarity.
• Experience working in agile environments with high engineering standards and operational rigor.
• Ability to break down complex problems, complete tasks independently, and guide others through technical decision-making.
• Excellent communication and collaboration skills across engineering, product, legal, and compliance collaborators.

Nice to Have

• Experience applying ML or AI to compliance, fraud detection, financial crime, abuse prevention, or policy enforcement.
• Experience fine-tuning LLMs (LoRA, PEFT, or similar techniques).
• Experience optimizing LLM inference workloads (GPU serving, batching, caching, routing strategies).
• Familiarity with AI governance, model risk management (MRM), or global AI regulatory frameworks.
• Experience building AI systems in large-scale marketplaces, fintech, or other regulated environments.
• Experience working with AI-assisted development workflows or agent-based tooling, with emphasis on validation, control, and quality rather than generation.