Product Manager, AI Revenue Systems

As a PM for Revenue, you'll own the data and execution layer that makes the GTM platform work: the scoring and routing systems that get the right accounts to the right people, the data infrastructure and acquisitions that power intelligence across the org, and the agents for SDRs, Solutions, and channel teams. This is the foundation that everything else depends on. Because your users are down the hall, feedback is immediate and iteration cycles are short. You'll build sharper product instincts faster here than in most roles — the kind that only come from shipping, seeing what lands, and doing it again. This role partners closely with Engineering, Data Science, Design, Finance, and Sales leadership.

What You’ll Do

• Build for GTM Teams. Own the agentic tooling and workflows for the GTM teams that are less served today — SDR prospecting and outreach, Solutions discovery and POV workflows, and channel partner execution. These motions are high-context and high-value, and largely greenfield.
• Own the GTM data platform. Define the data contracts, schemas, and pipelines that make GTM intelligence possible. Own account scoring, routing, and assignment — the systems that determine which accounts get attention, from whom, and when. Drive net new data acquisitions that expand what our agents can reason over. Ensure the data feeding our AI tools is accurate and trustworthy enough to act on.
• Serve every GTM motion, not just one. Resist the pull toward optimizing for a single team. Understand the distinct workflows and incentives across the GTM org and build systems flexible enough to serve all of them — while still being opinionated about what good looks like.
• Instrument, iterate, and close the loop. Define what good looks like. Build eval frameworks, feedback systems, and dashboards that tell you whether the tools are driving real adoption and impact — and use that signal to make reps active participants in improving the system over time.

What You’ll Need

• 1–3 years of product experience, or 3 - 5 years of total experience in a role that built real judgment — banking, consulting, deployment strategy, agent PM, GTM operator, or founder. The path matters less than what you built and what you learned.
• Deep hands-on experience building with AI: you've prototyped, shipped, and iterated on AI tools or agents — not just managed roadmaps about them. Technical fluency with modern AI coding harnesses (Cursor, Claude Code, Codex).
• Working knowledge of core LLM concepts (prompting, embeddings, retrieval, evals) and the judgment to translate these into reliable products where hallucinations have real consequences.
• Comfortable in SQL and confident reading data. You can pull your own analysis, spot what the numbers aren't telling you, and make decisions without waiting for a data team.
• Curious systems thinker who learns fast and defaults to building solutions.