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 $1.85B+ from the world’s top investors - including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, Bedrock, GIC, Elad Gil and Goldman Sachs—and was named one of America's best startup employers by Forbes.
About the role
Rippling is building core AI Platform infrastructure that will power intelligent and automated experiences across the company. This includes background agents, automated workflows, evaluation systems, data pipelines, model-quality loops, sandboxing, scaling, latency, reliability, and platform primitives that other product teams can build on.
As a Staff Software Engineer, you will operate as a hands-on technical leader. You will own ambiguous platform problems, lead cross-functional initiatives, shape architecture, and continue to write/review/debug production code. This role is not for someone who wants to only advise or delegate.
What you will do
- Own major AI Platform initiatives across backend, platform, infrastructure, and AI-enabled systems.
- Lead design and execution for systems supporting agents, evals, automation, data pipelines, model quality, sandboxing, scaling, latency, and reliability.
- Act as a technical lead for cross-functional work involving Product, Infra, Platform, AI, and Engineering teams.
- Break down ambiguous technical problems into clear architecture and execution plans.
- Write code, review critical code paths, debug production issues, and make implementation-level decisions.
- Build platform systems that can be reused by multiple product teams across Rippling.
- Raise the engineering bar through design reviews, code reviews, mentoring, and technical guidance.
- Balance speed, quality, reliability, and long-term platform maintainability.
Qualifications
- 10–15 years of software engineering experience, preferably in backend, platform, infrastructure, distributed systems, AI platform, or product engineering.
- Strong hands-on coding ability; must be willing to remain close to code, debugging, design reviews, and implementation details.
- Strong programming knowledge in one or more languages such as Python, Java, Go/Golang, C++, Scala, Kotlin, or C#.
- Proven experience building and operating large-scale distributed systems in production.
- Experience in high-growth or hyper-growth startup/product environments is strongly preferred.
- Strong system design and architecture fundamentals across APIs, databases, data modeling, concurrency, reliability, observability, and production operations.
- Ability to lead ambiguous, cross-functional technical initiatives without formal authority.
- Strong product intuition and ability to connect technical decisions to customer and business impact.
- Ability to mentor engineers and raise the technical bar while continuing to code.
- Practical exposure or strong curiosity around AI agents, LLM infrastructure, agentic workflows, ML/data infrastructure, inference pipelines, evals, fine-tuning, post-training, or automation systems.
Preferred qualifications
- Recent hands-on experience building AI agents, LLM tooling, AI infrastructure, automation platforms, eval systems, or model-quality systems.
- Experience with workflow orchestration, background automation, self-healing systems, reliability platforms, or internal developer platforms.
- Experience with permissions, approvals, auditability, compliance, security-sensitive workflows, or enterprise-grade controls.
- Strong Python and SQL knowledge.
- Experience with Go, Java, C++, or Scala in platform, infrastructure, distributed systems, or large-scale backend environments.
- Experience in AI-native, cloud/infra, platform-heavy, enterprise SaaS, fintech, HR tech, payroll, benefits, compliance, IT, or workflow-heavy companies.