Senior Software Engineer, Backend-Financial AI Product

Rippling•Seattle, WA

10d•$168,000 - $280,000•Onsite

About The Position

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. As a Senior Software Engineer on this team, you will help define how AI becomes a core part of financial product and infrastructure at Rippling.

Requirements

Strong software engineering fundamentals and experience building production systems at meaningful scale.
Experience designing backend systems, data models, APIs, workflows, or platform infrastructure for complex business domains.
Strong judgment around reliability, correctness, edge cases, and operational risk.
Interest in applied AI, LLMs, agents, evals, retrieval, structured extraction, or AI-assisted workflows.
Ability to reason clearly about where AI should be used, where deterministic systems should take over, and how to build guardrails around model-driven workflows.
Product sense and comfort operating in ambiguous problem spaces where the right answer is not obvious upfront.
Ability to partner with product managers, domain experts, operations teams, and engineers to turn messy real-world workflows into simple, scalable software.
A high-ownership mindset: you can take a problem from vague customer pain through architecture, implementation, rollout, measurement, and iteration.

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 product and platform primitives.
Help set the engineering bar for correctness, auditability, security, reliability, and production safety in AI-enabled and new AI native financial products.