Senior Software Engineer, Backend - Financial AI Product
ripplingBrooklyn (NY)
Senior Software Engineer, Backend - Financial AI Product
Posted today
ripplingBrooklyn (NY)
SENIORITY
Lead
About the role
Overview:
As a Senior Software Engineer on Rippling’s Financial AI Product Team, you will help embed AI into core financial workflows. You’ll build AI-powered capabilities for onboarding, reconciliation, and data handling, while ensuring deterministic, auditable financial execution. You will work with cross-functional partners to translate messy real-world data into reliable product features. This role offers deep impact in automating and safeguarding high-stakes financial processes at scale. Compensation / Benefits competitive salary benefits equity opportunity to work in-office equal opportunity employer accommodations available
Responsibilities:
Develop AI-powered capabilities for complex financial workflows (onboarding, data ingestion, reconciliation, validation, exception handling, operational reviews). Design systems that merge model-driven reasoning with deterministic financial execution. Create end-to-end tracing from financial values to source evidence and prevent execution when validation fails. Build ingestion and normalization pipelines for diverse data sources and formats. Develop evals, testing harnesses, observability, and feedback loops to measure AI quality in high-stakes workflows. Collaborate with product, design, operations, compliance, and platform teams to identify AI opportunities and turn them into reliable features. Prototype quickly in ambiguous areas and scale successful solutions into reusable primitives. Raise the bar for correctness, auditability, security, reliability, and production safety in AI-enabled financial products.
Key requirements:
Strong software engineering fundamentals with experience building production systems at meaningful scale. Experience designing backend systems, data models, APIs, workflows, or platform infrastructure for complex domains. Judgment about reliability, correctness, edge cases, and operational risk. Interest in applied AI, LLMs, agents, evals, retrieval, or AI-assisted workflows. Ability to reason about where AI should be used vs deterministic systems and to implement guardrails. Product sense and comfort in ambiguous problem spaces. Ability to partner with product managers, domain experts, operations teams, and engineers to simplify messy workflows. High-ownership mindset from problem to rollout and iteration. strong collaboration communication ownership AI/ML applications in production backend system design data modeling
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