Staff ML Engineer — Compliance Automation & Agentic AI

Fdetimes; United States; Hybride; USD 218 000 - 257 000; Plein temps

Avantages offerts par ce poste

Equity

Bonus eligibility

Résumé du poste

Coinbase is seeking a Staff Machine Learning Engineer for the Compliance Automation team. You will architect production Agentic AI systems that automate compliance workflows, ensure regulatorily defensible outputs, and lead ML direction across modeling, prompting, and evaluation.

You will partner with Compliance, Legal, and Operations to translate complex regulations into automated solutions, while mentoring engineers and maintaining a responsible AI approach with human oversight and measurable

Qualifications

• 8+ years in machine learning and software engineering with production AI experience.
• Deep expertise in LLM applications, prompting, grounding, and regulatorily defensible outputs.
• Experience designing evaluation frameworks for model monitoring and drift detection.

Responsabilités

• Architect and build production Agentic AI systems for compliance automation.
• Own ML direction for compliance agents, including model selection and guardrails.
• Design evaluation frameworks to measure agent performance and ensure auditable outputs.
• Translate regulatory workflows into automatable technical specs with stakeholders.
• Mentor engineers on ML system design and responsible AI practices.

Connaissances

Agentic AI

Prompt engineering

Retrieval-Augmented Generation

Grounding techniques

Hallucination reduction

Agent orchestration

Python proficiency

Outils

LangChain

Vector databases

Airflow

Spark

Description du poste

Coinbase is seeking a Staff Machine Learning Engineer for the Compliance Automation team. You will architect production Agentic AI systems that automate compliance workflows, ensure regulatorily defensible outputs, and lead ML direction across modeling, prompting, and evaluation.

You will partner with Compliance, Legal, and Operations to translate complex regulations into automated solutions, while mentoring engineers and maintaining a responsible AI approach with human oversight and measurable