Senior Software Engineer

Senior 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 role

AI 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 Senior Software Engineer on AI Governance, 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 problem.

What you will do

• Lead technical architecture for core AI governance systems, including MCP access control, model gateway policy, runtime authorization, audit pipelines, and usage attribution
• Design distributed systems that enforce policy at the moment of use without adding unnecessary latency or operational fragility
• Build identity-aware controls that connect AI usage back to Rippling's org graph, roles, permissions, apps, devices, and workforce data
• Own ambiguous product and technical problems across shadow AI detection, agent permissions, tool access, spend attribution, and data protection
• Partner with Product, Security, Legal, IT, and Engineering leaders to turn a new enterprise problem space into durable platform capabilities
• Set technical direction for a new team, including architecture reviews, design standards, reliability expectations, and long-term system boundaries
• Mentor senior engineers and raise the engineering bar for systems that must be secure, observable, and trusted by default.

What you will need

• 6+ years of professional software engineering experience, with a strong track record of technical leadership and org-wide impact
• Deep backend and distributed systems expertise, including experience designing services with clear ownership, strong reliability, and well-defined interfaces
• High agency and bias toward shipping, with the ability to turn ambiguous product ideas into narrow launches, fast feedback loops, and durable technical foundations
• AI-native engineering mindset, with hands-on experience using AI tools to move faster and the judgment to know when correctness, security, and architecture need deeper human ownership
• Strong security and systems judgment, especially around authorization, identity, data movement, audit logs, or policy enforcement
• Product sense for enterprise software, including a bias toward customer-visible impact and operational simplicity
• Ability to communicate clearly with engineering and non-engineering stakeholders, especially when navigating trade-offs across security, usability, and speed
• Familiarity with AI infrastructure, LLM gateways, MCP, developer tools, identity systems, or data protection is a strong plus, but not required