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Salary: USD 218,025 - 256,500 / annual
Coinbase is seeking a Staff Software Engineer to serve as a technical anchor for the Customer Experience (CX) sub-organization within Enterprise Applications and Architecture (EAA). This role operates across 8 globally distributed engineering squads that build and operate secure, Tier-1 platforms powering internal investigative and support workflows for approximately 7,000 specialists across Customer Experience, Compliance, Legal, Global Investigations, and Trust & Risk functions.
You will own the EAA-CX AI maturity and developer-experience roadmap in partnership with engineering leadership. This includes embedding with squads to surface opportunities, prototyping solutions on Coinbase's production AI infrastructure, and driving adoption through working demos and feedback loops. You'll lead platform excellence workstreams focused on improving reliability, observability, change-failure rate, and operational readiness for Tier-1 systems, partnering with engineering managers to ensure practices persist across teams.
Key responsibilities include defining build-versus-buy strategy for developer-experience and AI tooling across EAA-CX, evaluating what to build internally, adopt from partner teams, procure from vendors, or retire. You'll provide Staff-level architectural coverage across squads facing complex challenges, ramping in to land architecture or delivery while maintaining portfolio-wide coherence. Additionally, you'll build highly reliable, secure distributed backend services with full SLO ownership, observability, and incident response, ensuring automated outputs and production changes are traceable, reviewable, and safe in a regulated environment.
Required qualifications include 8+ years of software engineering experience with a demonstrated track record architecting and delivering large-scale distributed systems. You need deep proficiency in backend service design, API development (gRPC, MCP, GraphQL), and scalable microservices architectures. Strong experience with cloud-native infrastructure (AWS, Kubernetes, Terraform, CI/CD pipelines, event-driven architectures like Kafka) is essential. You should have a track record building secure, compliant internal tools or platforms with deep appreciation for security controls, access management, audit logging, and data governance in regulated environments. Experience building or integrating with agentic AI systems—including MCP servers, LLM orchestration layers, RAG pipelines, HITL workflows, and evaluation frameworks—is required, with proficiency in Go and willingness to work across Python.
