Senior Software Engineer, Backend (Consumer - Risk)

Coinbase

5d•Remote

About The Position

As a Senior Software Engineer on the Risk Engineering team within Coinbase's Consumer & Business group, you will build and optimize systems that detect and prevent fraud, manage credit and market risk, and protect users from financial threats in real time at crypto-market scale. You will provide technical leadership for end-to-end projects, make architectural decisions that shape risk detection and mitigation systems, and deliver complex features from inception through production. The work directly reduces financial loss and helps keep Coinbase users safe. Coinbase is a remote-first, but not remote-only company. Employees are expected to gather quarterly for in-person working sessions called “surges.”

Requirements

At least 5 years of backend software engineering experience delivering complex, high-impact systems in production environments.
Deep proficiency in Go, Ruby, Python, or Java.
Demonstrated experience designing distributed systems, including microservices, event-driven architectures, and REST or GraphQL APIs.
Ability to translate ambiguous requirements into clear technical plans and working architectures, and drive technical clarity across engineering, product, and data science teams.
Track record of leading end-to-end projects with long-term impact, including scoping, design, implementation, and monitoring, and mentoring engineers through the process.
Experience leading incident response, conducting post-mortems, and driving measurable operational improvements in high-availability systems.
Ability to use generative AI responsibly, maintain human oversight, and deliver business-ready outputs that improve 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 for risk detection and mitigation systems, translating ambiguous business requirements into scalable architectures that balance fraud prevention with user experience.
Partner with Data Science, ML, Risk Analysts, Product, and Compliance teams to build proactive systems, including real-time decisioning engines and model-driven fraud detection, as well as reactive solutions such as incident response tooling and compliance-driven risk controls.
Build AI-native risk systems that use fleets of agents to automate complex detection and response workflows and reduce manual intervention.
Mentor engineers, conduct code reviews, write technical design documents for complex systems, and champion engineering best practices.
Own system quality and operational excellence by addressing technical debt, triaging bugs, and leading incident response and post-mortems.