Staff Software Engineer, AI Developer Productivity

About the role

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.

As a founding Staff Software Engineer on the AI Developer Productivity within the InstitutionalProduct Group, you'll build and roll out agentic AI workflows that transform how Institutional engineers write, review, and ship code. This team is aligned with Coinbase's AI developer tooling vision but dedicated to the unique needs of Institutional's trading, custody, and financial infrastructure systems. You'll own the design and delivery of AI-powered capabilities that drive continuous code improvement, accelerate development cycles, and raise the quality bar across one of Coinbase's most critical product groups.

What you’ll do: 

• Own the architecture and delivery of agentic AI workflows for continuous code improvement, including automated refactoring, test generation, and proactive bug detection tailored to Institutional systems
• Build developer productivity tooling that integrates LLM-powered agents into the Institutional engineering workflow, from code review through deployment
• Partner with Platform's AI and DevX teams to align on shared infrastructure while customizing capabilities for Institutional's unique reliability, security, and regulatory requirements
• Drive adoption of AI-assisted development practices across Institutional engineering, measuring impact on velocity, code quality, and developer satisfaction
• Shape technical standards and best practices for human-AI collaborative development within the Institutional org, ensuring safe and governed use of AI agents in high-stakes financial systems

Required skills and experience:

• 8+ years of software engineering experience with at least 2 years building AI/LLM-powered developer tools, agentic workflows, or code automation systems
• Proven track record of designing and shipping production-grade systems that integrate LLMs or AI agents into software development lifecycles (e.g., code generation, automated review, CI/CD automation)
• Deep fluency in Java, Python, Go, or similar backend languages, with hands-on experience building and orchestrating AI agent frameworks (e.g., LangChain, LangGraph, or custom agent architectures)
• Demonstrated ability to partner cross-functionally with platform, infrastructure, and product teams to deliver tooling adopted by hundreds of engineers
• Familiarity with financial systems, high-reliability engineering, or regulated environments where correctness, auditability, and security are non-negotiable
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Position ID:  P78763

#LI-Remote

Pay Transparency Notice: Base salary varies by location (see range below). Total compensation may also include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)). 

Annual base salary range (excluding equity and bonus):

$218,025—$256,500 USD

• Application Limit: Candidates may submit a maximum of 3 applications within a 6-month period.
• Equal Opportunity Employer: Coinbase is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or genetic information. Applicants with criminal histories will be considered consistent with applicable federal, state, and local laws.
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