Product Operations | AI Revenue Systems

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role Most companies have more GTM ideas than they can reliably execute. Launching a new motion still requires manual targeting, weeks of enablement, seller coordination, and messy measurement. Learnings live in people's heads. When something works — or stops working — the playbook doesn't change fast enough. Ramp is building the AI platform that GTM runs on — a ground-up application layer where agents handle execution, playbooks encode institutional knowledge, and every interaction feeds a learning loop that makes the system smarter each cycle. We're operating at the frontier of what agents can do in a real enterprise context, and the problems are unsolved: reliable background execution at scale, human-agent collaboration that earns trust, and feedback loops that turn raw GTM signal into a compounding organizational advantage. 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 product, 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. You connect dots across AI, data, and GTM quickly, pick up new domains without needing to be an expert first, and don't wait for perfect requirements to ship. - Comfort with 0-to-1 ambiguity. There is no established playbook here. You define the problems, prioritize ruthlessly, and build the foundation others will bui

1–3 years of product experience (or 3–5 years in related roles such as banking, consulting, deployment strategy, agent PM, GTM operator, or founder). Deep hands-on experience building with AI: prototyped, shipped, and iterated on AI tools or agents. Technical fluency with modern AI coding harnesses. Working knowledge of core LLM concepts (prompting, embeddings, retrieval, evals). Comfortable with SQL and reading data to analyze independently. Curious systems thinker who learns fast, builds, and ships even with imperfect requirements. Comfortable with 0-to-1 ambiguity and defining problems, prioritizing ruthlessly.

Own the data and execution layer for the GTM platform: account scoring, routing, and assignment; data contracts, schemas, and pipelines powering GTM intelligence; agents for SDRs, Solutions, and channel teams. Build for GTM teams by developing agentic tooling and workflows for SDR prospecting/outreach, Solutions discovery/POV workflows, and channel partner execution. Ensure data quality and trustworthiness for AI tools; drive data acquisitions to broaden what agents can reason over. Create a flexible system that serves multiple GTM motions, not just one. Instrument, iterate, and close the loop with eval frameworks, dashboards, and feedback mechanisms to drive adoption and impact.