Senior Software Engineer, Backend Full Stack - Financial AI Product

About the role

Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system. Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third‑party apps like Slack and Microsoft 365—all within 90 seconds. Based in San Francisco, CA, Rippling has raised $1.4B+ from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America's best startup employers by Forbes. We prioritize candidate safety. Please be aware that all official communication will only be sent from @Rippling.com addresses.

Rippling’s Financial Product team builds the systems that power some of the most critical financial workflows for modern businesses — including payroll, payments, reconciliation, compliance, and operational finance. These products sit at the intersection of money movement, employee trust, customer operations, and regulatory complexity. The work is high stakes: financial systems must be accurate, reliable, auditable, and resilient, even as they handle messy real‑world data and complex business rules across customers, countries, and use cases. We are using AI to make these workflows dramatically simpler. That means building systems that can interpret inconsistent data, automate manual review, surface exceptions, explain outcomes, and accelerate implementation — while preserving the deterministic controls financial products require. The goal is not AI for its own sake; it is practical, production‑grade AI that helps customers run financial workflows with more speed, confidence, and accuracy.

Responsibilities

• Build AI‑powered product capabilities for complex financial workflows such as onboarding, data ingestion, reconciliation, validation, exception handling, and operational review.
• Design systems that combine model‑driven reasoning with deterministic financial execution.
• Build workflows that trace financial values back to source evidence, surface unresolved gaps, and prevent downstream execution when validation fails.
• Create ingestion and normalization systems that can handle messy exports, historical records, customer‑provided files, and inconsistent provider formats.
• Develop evals, testing harnesses, observability, and feedback loops to measure AI quality in workflows where “almost always right” is not good enough.
• Partner closely with product, design, operations, compliance, and platform teams to identify high‑impact AI opportunities and turn them into reliable product capabilities.
• Work through ambiguous product areas, prototype quickly, and scale successful solutions into reusable