About the role
About
the role Rippling AI helps businesses manage HR, IT, Finance, and Payroll — not by generating plausible responses, but by writing real queries against live data, showing its work, and taking real action on behalf of customers. The hard problem now is making it trivially easy for any Rippling product team to teach the assistant new capabilities and scaling it to customers of all sizes. In this role, you will own the platform layer that powers everything the AI Assistant can do: the frameworks product teams use to add new capabilities, the orchestration and routing layer that selects the right tools and models, and the economics of every query. You'll partner with the AI platform engineering team to turn Rippling AI assistant into an autonomous agent platform that takes real action across every Rippling product. What you will do Own the AI Platform roadmap , including the skills framework, routing, tool orchestration, and the reasoning loop that powers multi-step agent tasks. Make it trivially easy for any product team to ship AI capabilities — from read-only queries to write actions that stage payroll runs, grant system access, or reassign employees, all with proper permissions and approval flows. Drive model selection strategy across providers, deciding when to use which model, at what cost, for what quality bar. Define the evaluation and improvement strategy — how we measure agent quality at scale, catch regressions, and continuously improve with real usage data. Partner with 10+ product teams (HR, Payroll, IT,
Benefits, Finance) to onboard their domains onto the platform and make self-serve adoption easy. Shape the long-term vision for autonomous, long-running agents that do meaningful work on behalf of customers beyond the chat interface What you will need 8+ years in product management, with at least 2 years shipping LLM-powered or conversational AI products to production at scale. Deep technical fluency in LLM systems — can evaluate RAG architectures, prompt engineering approaches, fine-tuning tradeoffs, model benchmarking, and reinforcement learning strategies. Strong opinions on cost, latency, and quality tradeoffs in AI systems — you've managed the economics of inference at scale. B2B SaaS experience — you understand enterprise buyers, compliance
