Staff Software Engineer, AI Developer Productivity

Coinbase

4h•$218,025 - $256,500•Remote

About The Position

As a founding Staff Software Engineer on the AI Developer Productivity team within Coinbase’s Institutional Product Group, you will build and roll out agentic AI workflows that transform how Institutional engineers write, review, and ship code. The team supports Coinbase’s AI developer tooling vision while addressing the specific needs of Institutional trading, custody, and financial infrastructure systems. You will own the design and delivery of AI-powered capabilities that support continuous code improvement, accelerate development cycles, and raise quality across the Institutional Product Group. Coinbase is remote-first, but not remote-only. Employees are expected to gather quarterly for in-person working sessions called “surges.”

Requirements

At least 8 years of software engineering experience, including at least 2 years building AI/LLM-powered developer tools, agentic workflows, or code automation systems.
A proven track record of designing and shipping production-grade systems that integrate LLMs or AI agents into software development lifecycles, such as code generation, automated review, or CI/CD automation.
Deep fluency in Java, Python, Go, or similar backend languages, with hands-on experience building and orchestrating AI agent frameworks such as 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 essential.
Responsible use of generative AI, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Responsibilities

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 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 organization, ensuring safe and governed use of AI agents in high-stakes financial systems.