Staff Software Engineer - Fraud

Every new business that applies to Mercury is like a new star appearing in the night sky. On its own, it’s a single point of light. But when we look closer, patterns emerge—data trails from partners, filings, founders, and financial histories—all connecting to form a larger constellation.

That’s what our Risk product engineering teams do at Mercury. We guide thousands of business applications through our systems—each one unique, each one needing a smooth and trustworthy landing. The challenge: keep everything moving fast without compromising safety. Every day, our work helps founders open their first account, launch their next idea, and accelerate their growth like rocketships.

Our mission is to build the intelligent, automated systems and operational tools that make this possible—where machine learning, AI, and human judgment work seamlessly together to power the next generation of business banking*. We use intelligence to detect risks earlier, make real-time decisions with confidence, and enable instant, delightful account approvals that keep pace with the builders we serve.

When we do it right, the result is quiet brilliance: onboarding that feels effortless, even though it’s powered by galaxies of data, precision, and care.

We’re looking for a Staff Software Engineer to contribute with building the systems and tools that make it all happen—from application approvals to ongoing and enhanced due diligence—ensuring every business that joins Mercury is both safe and their experience is delightful.

As part of this role, you will:

• Lead the architecture, implementation, and long-term roadmap for core systems which support multiple fraud prevention use cases.

• Own the end-to-end delivery of large cross-function projects, translating ambiguous high impact problems into strategy and execution, make pragmatic tradeoffs, and drive teams to measurable outcomes.

• Design, build, and operate highly available, low-latency, backend systems that enable real-time scoring and decisioning for fraud prevention.

• Partner with Data Science and ML teams to productionize models, build reliable ML data pipelines, and enable real-time model serving.