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Salary: USD 218,025 - 256,500 / annual
Coinbase is seeking a Staff Machine Learning Engineer to lead the design and delivery of production ML systems within CEE Intelligence (CEEI), the ML and intelligence layer powering Coinbase's customer engagement ecosystem. This role focuses on building customer intelligence, evaluation systems, routing and classification models, safety guardrails, and decisioning capabilities that support Help Center, chat, voice, agent tooling, and other customer support runtimes.
You will own the end-to-end design and delivery of production ML systems, applying LLMs, deep learning, graph neural networks, and classical ML techniques to solve complex customer experience problems. You'll partner with leadership and cross-functional stakeholders to define strategic roadmaps and translate vision into quarterly execution plans with measurable outcomes. Leading high-impact ML projects from architecture through production, you'll manage priorities, risks, and dependencies while building scalable, secure, and reliable ML systems, evaluation pipelines, and data workflows using high-quality, well-tested Python code.
A key aspect of this role is mentoring engineers and establishing team-wide standards for design, testing, observability, and operational excellence. You'll drive design reviews covering security, responsible AI, model quality, and architectural integrity, improving reliability, scalability, and latency across the stack. The role emphasizes responsible use of generative AI, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.
Coinbase is a remote-first but not remote-only company. Expect to gather quarterly for intense in-person working sessions called "surges." The environment is high-intensity and mission-driven, focused on increasing economic freedom through cryptocurrency and blockchain technology.
**Requirements:**
- 8+ years of experience in machine learning and software engineering, with proven success building, scaling, and maintaining production ML systems, data platforms, or analytical services
- Hands-on expertise with one or more of: LLMs, deep learning, graph neural networks, gradient-boosted trees, or regression, combined with strong knowledge of NLP, information retrieval, data mining, or advanced statistics
- Strong Python skills with demonstrated ability to write maintainable, highly tested production code for ML services
- Experience mentoring engineers and raising technical standards across a team or organization, including leading design reviews and influencing technical and non-technical stakeholders
- Ability to utilize generative AI responsibly, maintaining human oversight to deliver business-ready outputs
