Staff Software Engineer

Staff Software Engineer - AI Governance 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 $1.4B+ from the world's top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America's best startup employers by Forbes. We prioritize candidate safety. Please be aware that all official communication will only be sent from @Rippling.com addresses. About the rol eAI is changing how companies work, but most enterprises still lack a clear way to govern it. Employees are using AI apps directly. Developers are routing code through agents. Business teams are connecting models to SaaS tools through Model Context Protocol (MCP) servers. Companies are spending more on AI every month, often without a reliable view into who is using what, what data is leaving the company, or which agents have access to sensitive systems .The AI Governance team is building Rippling's answer to that problem .This team sits at the intersection of identity, access control, model routing, MCP security, data protection, spend management, and auditability. The goal is to give companies one governed path for AI usage across employees, agents, models, and business tools . As a Staff Software Engineer on AI Governan ce, you will help define and build a new product category. You will work on systems that make AI usage attributable, policy-aware, budget-aware, and auditable at runtime. This is a high-ambiguity, high-impact role for someone who wants to build foundational platform technology while staying close to a rapidly evolving customer proble m. What you will doLead technical architect ure for core AI governance systems, including MCP access control, model gateway policy, runtime authorization, audit pipelines, and usage attribut ionDesign distributed syst ems that enforce policy at the moment of use without adding unnecessary latency or operational fragil ityBuild identity-aware contr ols that connect AI usage back to Rippling's org graph, roles, permissions, apps, devices, and workforce d ataOwn ambiguous product and technical probl ems across shadow AI detection, agent permissions, tool access, spend attribution, and data protect ionPartner with Product, Security, Legal, IT, and Engineering lead ers to turn a new enterprise problem space into durable platform capabilit iesSet technical direct ion for a new team, including architecture reviews, design standards, reliability expectations, and long-term system boundar iesMentor senior engine ers and raise the engineering bar for systems that must be secure, observable, and trusted by defau lt. What you will need8+ years of professional software engineering experi ence, with a strong track record of technical leadership and org-wide im pactDeep backend and distributed systems exper tise, including experience designing services with clear ownership, strong reliability, and well-defined interf acesHigh agency and bias toward ship ping, with the ability to turn ambiguous product ideas into narrow launches, fast feedback loops, and durable technical foundat ionsAI-native engineering min dset, with hands-on experience using AI tools to move faster and the judgment to know when correctness, security, and architecture need deeper human owner shipStrong security and systems judg ment, especially around authorization, identity, data movement, audit logs, or policy enforce mentProduct sense for enterprise soft ware, including a bias toward customer-visible impact and operational simpli cityAbility to communicate cle arly with engineering and non-engineering stakeholders, especially when navigating trade-offs across security, usability, and s peedFamiliarity with AI infrastructure, LLM gateways, MCP, developer tools, identity systems, or data protec tion is a strong plus, but not requ ired