Staff ML Engineer, CX Intelligence — Remote-First Impact

Coinbase, Inc.; Northern (KY); Remote; USD 218,000 - 257,000; Full time

Job summary

Coinbase is a remote-first company building the future of finance. We seek a Staff Machine Learning Engineer to own the design and delivery of production ML systems across customer experience challenges. You will partner with leadership to convert vision into quarterly execution plans with measurable outcomes.

You will mentor engineers, raise standards, and drive reviews for security, model quality, and system reliability, ensuring responsible AI and scalable performance across the stack.

Qualifications

• 8+ years of ML and software engineering in production systems.
• Experience with LLMs, deep learning, graph neural networks, or gradient-boosted trees.
• Strong Python and production-grade code testing practices.
• Experience mentoring engineers and leading design reviews.
• Demonstrates responsible AI with human oversight.

Responsibilities

• Own design and delivery of production ML systems across customer-experience problems.
• Partner with leadership to define strategic roadmaps and quarterly plans.
• Lead high-impact ML projects end-to-end from architecture to production.
• Build scalable, secure ML systems and data workflows with well-tested Python.
• Mentor engineers and establish team-wide standards for quality and observability.
• Lead security, responsible AI, and architectural reviews to improve reliability.

Skills

ML systems design

LLMs & NLP

Python production code

Mentoring engineers

Responsible AI

Security & observability

Tools

Python

Job description

Coinbase is a remote-first company building the future of finance. We seek a Staff Machine Learning Engineer to own the design and delivery of production ML systems across customer experience challenges. You will partner with leadership to convert vision into quarterly execution plans with measurable outcomes.

You will mentor engineers, raise standards, and drive reviews for security, model quality, and system reliability, ensuring responsible AI and scalable performance across the stack.