Software Engineer, Risk

The Risk Engineering team builds the systems that enable Ramp to move quickly without compromising trust. As an engineer on Risk, 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 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 Risk 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’ tenure on Ramp.

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

2+ years of backend engineering experience.

Proficiency in Python or a comparable backend language.

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.

Interest in financial optimization problems.

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

Defensive programming aptitude.

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

If you are being referred for the role, please contact that person to apply on your behalf.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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