Senior Staff Software Engineer - Rippling AI

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.
• 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 and 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 reusable by product teams.
• Help shape the long-term technical foundation for Rippling AI.

Requirements

• 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, including designing, reviewing, debugging, and writing code regularly.
• Strong programming knowledge in one or more languages such as Python, Java, Go, C++, Scala, Kotlin, or C#.
• Experience owning broad, ambiguous technical charters across multiple systems or teams.
• Strong architecture and system design depth across large-scale production systems.
• 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.
• Strong product and platform judgment, with the ability to connect technical decisions to business and customer impact.
• Ability to mentor and elevate engineers across levels.
• AI/ML background is a plus but is not mandatory; practical exposure or adjacent experience in AI platform infrastructure, agents, evals, ML/data infrastructure, model infrastructure, inference, sandboxing, workflow automation, scaling, latency, or reliability is valuable.

Preferred Qualifications

• Experience building AI platform infrastructure, evaluation 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, infrastructure, platform, or distributed-systems environments.
• Experience building internal platforms used by multiple product teams.
• Experience in high-growth startups or fast-moving product-led technology companies.
• Exposure to enterprise SaaS, fintech, HR technology, payroll, benefits, compliance, IT, Data Cloud, or workflow-heavy platforms.
• Ability to operate as a builder-leader who can define direction, align stakeholders, and still build.

Compensation and Benefits

• Competitive salary, benefits, and equity.
• Compensation considers professional background, experience, and location; final offers may vary.