Software Engineer, Credit

The Credit Engineering team builds the systems that enable Ramp to manage credit lifecycles for businesses end-to-end, from initial underwriting through ongoing portfolio strategy optimization. As an engineer on Credit, you’ll own systems that influence $100+ billion in annual payment volume through millions of decisions across our product stack in support of the most ambitious FinTech portfolio in the United States.

We are looking for a backend engineer to own the technical roadmap for underwriting, limit management, portfolio optimization, and agentic workflows. You will build robust systems, automate complex operational tasks, and maintain the high-frequency controls that power all of Ramp’s products. If you are obsessed with correctness, excited by ambiguity, and passionate about problems at the intersection of distributed systems and financial strategy, this role is for you.

- Architect scalable, stateful systems for automated limit management to optimize Ramp’s charge card portfolio.

- Envision and build the next generation of agents and tools that automate manual credit decisions and increase operational efficiency.

- Develop high-volume, low-latency risk controls to support Ramp’s expansion into new products, customer segments, and geographies.

- Design and build real-time decisioning systems spanning the entirety of a business's credit lifecycle on Ramp.

- Partner closely with analysts, data scientists, and product to translate risk strategies into reliable software.

- Experience working with distributed systems and async frameworks.

- Proficiency with converting abstract business problems into robust data models and technical specs.

- Experience building or operating systems with strict auditability, observability, and correctness guarantees.

- High ownership mentality – you care deeply about the systems you build.

- Ability to work closely with non-engineering partners (Risk Strategy, Ops, Sales) to create impact.

- Experience leveraging AI/LLMs for development or for internal workflows.

- Willingness to learn and move fast while holding the bar on correctness.

- Experience using feature stores, rule engines, and ML platforms in production.