Member of Technical Staff - Senior Product Manager

Patronus AI, Inc.•San Francisco, CA

14h•$200,000 - $300,000•Onsite

About The Position

About Patronus AI Patronus AI is a frontier lab developing simulation research and infrastructure to accelerate progress toward human-aligned AGI. We are on a mission to simulate all of the world’s intelligence. We are the team behind some of the earliest and most influential research in AI evaluation like FinanceBench, Lynx, SimpleSafetyTests, CopyrightCatcher, Humanity’s Last Exam, and more. We are formerly AI researchers and engineers from companies like Meta AI, Amazon AGI, and Google. Our customers include foundation model labs and Fortune 500 enterprises like Adobe. We are backed by top-tier investors like Lightspeed Venture Partners, Notable Capital, Stanford University, Noam Brown, Gokul Rajaram, and more.

Requirements

BS, MS, or equivalent experience in Computer Science, Engineering, or another technical / quantitative field, with 8+ years as a product manager.
Experience PMing platform, infrastructure, or internal tools — products whose customers are engineers and internal teams rather than external end users. This is required, not preferred.
A track record of PRDs that engineers actually built from — and of shipping when priorities moved after the spec was written, without letting quality or trust in the spec slip.
Strong technical fluency, including daily use of AI tools, comfort reading or reviewing code, and the ability to pull and analyze your own usage data. You should be able to go deep enough into the work to have credibility with engineers.
Evidence of ruthless prioritization: MVPs shipped, cut lists published, and features you killed with the reasoning to back it up.
Clear written and verbal communication, including the ability to turn messy stakeholder asks into a spec with crisp requirements, owners, and dates.
Strong eye for quality and detail, with a bias toward catching gaps, inconsistencies, and subtle failure modes before engineering does.

Nice To Haves

Experience with reinforcement learning, agent evaluation, RL environments, or human-data / SME-sourced data pipelines.
Experience building products whose primary users are AI agents rather than humans.
0→1 experience at a fast-growing startup.

Responsibilities

Own the roadmap for the internal platform and tools end-to-end — the environment platform, agentic build and QA tooling, expert onboarding, delivery tracking, and the spec pipeline. Sequence it against the company OKR roadmap so product is never the thing engineering is waiting on.
Write PRDs engineers trust and build from. Versioned and frozen at kickoff, explicit about what's in the MVP and what's deliberately cut, and honest about buildability constraints (e.g., which classes of apps our agentic build tooling can and cannot handle today).
Work with, identify the pain points of, and brainstorm solutions with internal customers — environment engineers, SME ops, QA, and GTM. Then turn what you learn into a ranked backlog and tell them plainly what's not getting built and why.
Make usage data the basis for decisions. Instrument the tools, watch what agents and people actually do in them, and kill features that don't earn their keep.
Close the loop between QA and spec. When automated QA keeps surfacing the same class of missing feature, that's a spec failure — fix the pipeline, not just the ticket.
Apply the "if an agent can do it" rule to your own function: build agentic workflows for spec generation, review, and versioning, and spend your human hours on the judgment calls only you can make.
Ruthlessly scope. Ship the smallest thing that changes an internal customer's week, publish the cut list, and iterate from evidence.
Stay hands-on. Run agents in the environments, poke at the tools, prototype with AI, and build small evals to confirm whether a feature actually worked.