Product Operations for AI-Driven GTM & Revenue

Confidential; United States; Remote; USD 130,000 - 190,000; Full time

Benefits offered by this job

Flexible PTO

Home-office equipment ordering

Health and wellness stipend

Intra-office travel budget

Weekly coffee stipend

One Medical annual membership

Relocation to NYC or SF

Job summary

RampRamp is building the AI-powered GTM platform that underpins revenue operations. As a PM for Revenue, you’ll own data and execution layers, scoring and routing, and the data infrastructure powering intelligence across the org.

You’ll work closely with Engineering, Data Science, Design, Finance, and Sales leadership to ship reliable AI-enabled workflows. You’ll define contracts, build dashboards, and iterate quickly in a fast-moving, high-ambiguity environment where feedback loops drive

Qualifications

• Deep hands-on experience building with AI: prototyped, shipped, and iterated on AI tools or agents.
• Technical fluency with modern AI coding harnesses (Cursor, Claude Code, Codex).
• Working knowledge of core LLM concepts (prompting, embeddings, retrieval, evals).

Responsibilities

• Own the data and execution layer for the GTM platform, including scoring and routing systems.
• Define data contracts, schemas, and pipelines powering GTM intelligence.
• Ensure reliable, trustworthy data feeding AI tools and drive cross-team adoption.
• Collaborate with Engineering, Data Science, Design, Finance, and Sales leadership.

Skills

AI prototyping

LLM concepts

SQL

Data analysis

Cross-functional collaboration

Product management

Tools

Cursor

Claude Code

Codex

Job description

RampRamp is building the AI-powered GTM platform that underpins revenue operations. As a PM for Revenue, you’ll own data and execution layers, scoring and routing, and the data infrastructure powering intelligence across the org.

You’ll work closely with Engineering, Data Science, Design, Finance, and Sales leadership to ship reliable AI-enabled workflows. You’ll define contracts, build dashboards, and iterate quickly in a fast-moving, high-ambiguity environment where feedback loops drive