Senior Product Manager — AI Agent Platform

Patronus AI; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Patronus AI in San Francisco is seeking a Senior Product Manager to own the platform, infrastructure, and internal tools that let a small team turn frontier-lab demand into delivered RL environments. You will define a versioned, MVP-focused spec process and align with the OKR roadmap for engineering reliance on stable PRDs.

You will work with environment engineers, QA, and GTM to translate learnings into a ranked backlog, measure usage to guide decisions, and push for agentic workflows.

Qualifications

• 8+ years as a product manager with platform/infrastructure focus.
• Experience PMing products for engineers and internal teams.
• Track record of PRDs engineers actually built from.
• Strong technical fluency and ability to analyze usage data.
• Evidence of ruthless prioritization and clear written/ verbal communication.

Responsibilities

• Own the roadmap for the internal platform and tools end-to-end — environment platform, agentic build and QA tooling, expert onboarding, delivery tracking, and the spec pipeline.
• Write PRDs engineers trust and build from; versioned and frozen at kickoff with MVP scope.
• Collaborate with internal customers to identify pain points and translate into a ranked backlog.
• Make usage data the basis for decisions and kill features that don’t earn their keep.
• Close the loop between QA and spec; fix the pipeline rather than just the ticket.
• Apply agentic workflows for spec generation, review, and versioning; focus human hours on judgment calls.
• Ruthlessly scope; publish the cut list and iterate from evidence.
• Stay hands-on; run agents, prototype with AI, and build small evals to confirm features work.

Skills

Platform product management

Engineering collaboration

Data-driven decision making

Stakeholder communication

Education

BS/MS in CS or related

Job description

Patronus AI in San Francisco is seeking a Senior Product Manager to own the platform, infrastructure, and internal tools that let a small team turn frontier-lab demand into delivered RL environments. You will define a versioned, MVP-focused spec process and align with the OKR roadmap for engineering reliance on stable PRDs.

You will work with environment engineers, QA, and GTM to translate learnings into a ranked backlog, measure usage to guide decisions, and push for agentic workflows.