Senior Staff Software Engineer - Rippling AI

About Rippling 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 parts of **Rippling AI** and the **AI Platform** . These foundational systems will support AI capabilities across Payroll, Benefits, HR Products, Talent, Spend, IT, Data Cloud, and other product areas.

As a **Senior Staff Software Engineer** , you will own broad, ambiguous, high-impact technical charters across AI platform infrastructure. Key areas include harness engineering, eval frameworks, model-related infrastructure, selective model training work, sandboxing, scaling, latency, reliability, background agents, and enterprise intelligence systems.

The team is looking for extremely strong, hands-on engineers. AI/ML background is a strong plus, but not mandatory. The strongest signal is deep backend/systems engineering foundation, high intellectual horsepower, ability to move fast in ambiguity, and willingness to design, review, debug, and write code regularly.

What You Will Do

• Own broad AI Platform charters across multiple systems and teams.
• Lead architecture and execution for foundational systems supporting AI capabilities across Rippling.
• Build and guide work across AI platform infrastructure, harness engineering, eval frameworks, model-related infrastructure, sandboxing, scaling, latency, and reliability.
• Support AI capabilities across Payroll, Benefits, HR Products, Talent, Spend, IT, and Data Cloud.
• Lead without formal authority while staying deeply connected to execution.
• Write code, review critical code paths, debug complex production issues, prototype solutions, and make implementation-level decisions.
• Partner with Product, Infra, Platform, AI, and senior engineering stakeholders to convert ambiguous AI/product bets into scalable systems.
• Mentor Staff, Senior, and SE II engineers while raising engineering standards across design, coding, debugging, reliability, and operational excellence.
• Define technical patterns and platform primitives that can be reused by product teams across Rippling.
• Help shape the long-term technical foundation for Rippling AI.

Qualifications

• 14+ years of software engineering experience, depending on quality of scope and impact.
• Strong backend, platform, infrastructure, distributed systems, or systems engineering foundation.
• Deep hands-on coding ability; must be able to design, review, debug, and write code regularly.
• Strong programming knowledge in one or more languages such as Python, Java, Go/Golang, C++, Scala, Kotlin, or C#.
• Proven experience owning broad, ambiguous technical charters across multiple systems or teams.
• Strong architecture and system design depth across large-scale production systems.
• Strong technical judgment across scale, reliability, latency, observability, APIs, data modeling, concurrency, and production operations.
• Ability to influence and lead without formal authority while staying close to implementation.
• High intellectual horsepower, fast learning ability, and comfort operating in ambiguity.
• Strong product/platform judgment and ability to connect technical decisions to business and customer impact.
• Ability to mentor and elevate engineers across levels.
• AI/ML background is a strong plus, but not mandatory; practical exposure or strong adjacent experience in AI platform infrastructure, agents, evals, ML/data infra, model infrastructure, inference, sandboxing, workflow automation, scaling, latency, or reliability is highly valuable.

Preferred Qualifications

• Prior experience building AI platform infrastructure, eval frameworks, harness systems, LLM tooling, model-quality systems, or ML/data…