Senior Software Engineer, Backend (Consumer - Risk)

Coinbase

14h•Remote

About The Position

As a Senior Software Engineer on the Risk Engineering team within the Consumer & Business group, you'll build and optimize the systems that detect and prevent fraud, manage credit and market risk, and protect millions of users from financial threats in real time at crypto-market scale. You'll provide technical leadership for end-to-end projects, drive architectural decisions that shape our risk detection and mitigation systems, and deliver complex features from inception through production. Your work directly reduces financial loss and keeps Coinbase users safe.

Requirements

5+ years of backend software engineering experience with a track record of delivering complex, high-impact systems in production environments
Deep proficiency in backend languages (Go, Ruby, Python, or Java) with demonstrated experience designing distributed systems including microservices, event-driven architectures, and REST/GraphQL APIs
Proven ability to translate ambiguous requirements into clear technical plans and working architectures, driving technical clarity across engineering, product, and data science teams
Track record leading end-to-end projects with long-term impact, including scoping, design, implementation, monitoring, and mentoring other engineers through the process
Experience leading incident response, conducting post-mortems, and driving measurable operational improvements in high-availability systems
Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Responsibilities

Lead the design and delivery of complex risk platform features end-to-end, from architecture through implementation, deployment, and monitoring, with stringent requirements for correctness, performance, and reliability
Drive architectural decisions that shape Coinbase's risk detection and mitigation systems, translating ambiguous business requirements into scalable working architectures that balance fraud prevention with user experience
Partner cross-functionally with Data Science, ML, Risk Analysts, Product, and Compliance teams to build proactive systems (real-time decisioning engines, model-driven fraud detection) and reactive solutions (incident response tooling, compliance-driven risk controls)
Build AI-native risk systems that leverage fleets of agents to automate complex detection and response workflows, significantly reducing the need for manual intervention
Strengthen the team by mentoring engineers, conducting rigorous code reviews, writing technical design documents for complex systems, and championing engineering best practices across the codebase
Own system quality and operational excellence by proactively addressing technical debt, driving bug triaging, and leading incident response and post-mortems to ensure platform reliability