As a Staff Machine Learning Engineer on the Compliance Automation team within the Platform group, you'll build the AI systems that are reimagining how Coinbase executes compliance operations at scale. This team designs and ships Agentic AI solutions that automate compliance processes, from investigation workflows to decision support, replacing manual operations with intelligent, auditable, and measurable automation. You'll own the ML architecture for production compliance agents, driving technical decisions on model selection, grounding, guardrails, and evaluation while partnering with Compliance stakeholders to ensure every automated output meets regulatory standards.

What You'll Do:

• Architect and build production Agentic AI systems for compliance automation, including LLM-powered agents, orchestration pipelines, and decision support workflows that replace manual compliance processes
• Own the ML technical direction for compliance agents, driving model selection, prompt engineering, grounding strategies, and guardrail design that ensure agent outputs are accurate, auditable, and regulatorily defensible
• Design and implement evaluation and measurement frameworks that quantify agent performance, detect hallucinations and drift, and provide the auditability compliance teams and regulators require
• Partner with Compliance, Legal, and Operations stakeholders to translate complex regulatory workflows into automatable technical specifications, identifying which processes are candidates for full automation vs. human-in-the-loop augmentation
• Mentor engineers on ML system design, LLM application patterns, and responsible AI practices, raising the team's technical bar for production AI systems

Required Skills and Experience:

• 8+ years in machine learning and software engineering, with demonstrated experience shipping LLM-based or Agentic AI applications to production
• Deep expertise in LLM application development, including prompt engineering, retrieval-augmented generation (RAG), grounding techniques, hallucination reduction, and agent orchestration frameworks
• Proven track record building evaluation and measurement systems for ML models in production, including monitoring for drift, accuracy degradation, and output quality at scale
• Strong Python proficiency with hands-on experience in GenAI frameworks and ML infrastructure (e.g., LangChain, vector databases, Airflow, Spark, or equivalent orchestration and data processing tools)
• Experience in compliance, regulatory technology, financial crimes, or similarly regulated domains where auditability, explainability, and correctness of automated decisions are critical
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality