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Rippling is building Rippling AI and a core AI Platform to 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 responsibilities include:
- 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
The team is looking for extremely strong, hands-on engineers. AI/ML background is a strong plus but not mandatory. The strongest signal is a deep backend/systems engineering foundation, high intellectual horsepower, ability to move fast in ambiguity, and willingness to design, review, debug, and write code regularly.
Rippling highly values in-office collaboration; office-based employees are expected to work in the office at least three days per week.
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: 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
HIGHLY PREFERABLE (NOT REQUIRED):
- 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
