About this role

▸about Coinbase — company background

Ready to do the most impactful work of your career? At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase.

Staff Machine Learning Engineer, CX Intelligence

CEE Intelligence (CEEI) is the ML and intelligence layer for Coinbase's customer engagement ecosystem, building customer intelligence, evaluation systems, routing and classification models, safety guardrails, and decisioning capabilities that power Help Center, chat, voice, agent tooling, and other customer support runtimes. As a Staff Machine Learning Engineer, you'll lead the design and delivery of production ML systems across the org, shaping technical strategy, building scalable intelligence capabilities, and partnering with product and engineering stakeholders to improve resolution quality, automation coverage, safety, and customer experience.

What you'll do:

Own the design and delivery of production ML systems, applying LLMs, deep learning, graph neural networks, and classical ML techniques to solve complex customer experience problems.

Partner with leadership and cross-functional stakeholders to define strategic roadmaps and translate vision into quarterly execution plans with measurable outcomes.

Lead high-impact ML projects end-to-end, from architecture through production, managing priorities, risks, and dependencies.

Build scalable, secure, and reliable ML systems, evaluation pipelines, and data workflows using high-quality, well-tested Python code.

Mentor engineers and establish team-wide standards for design, testing, observability, and operational excellence.

Drive design reviews covering security, responsible AI, model quality, and architectural integrity, improving reliability, scalability, and latency across the stack.

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