Senior Staff Software Engineer, AI

Rippling•San Francisco, CA

7d•Onsite

About The Position

Rippling is building Rippling AI and the core AI Platform. These foundational systems will support AI capabilities across Payroll, Benefits, HR Products, Talent, Spend, IT, Data Cloud, and among 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, agent primitives, 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.

Requirements

12+ 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 operate as a builder-leader: someone who can define direction, align stakeholders, and still build.
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.

Nice To Haves

Prior experience building AI platform infrastructure, eval frameworks, harness systems, LLM tooling, model-quality systems, or ML/data infrastructure.
Experience with sandboxing, inference systems, scaling, latency optimization, reliability engineering, or large-scale distributed platforms.
Strong Python and SQL knowledge.
Experience with Go, Java, C++, or Scala in backend, infra, platform, or distributed systems environments.
Experience building internal platforms used by multiple product teams.
Prior experience in high-growth / hyper-growth startups or fast-moving product-led technology companies.
Exposure to enterprise SaaS, fintech, HR tech, payroll, benefits, compliance, IT, Data Cloud, or workflow-heavy platforms.

Responsibilities

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.