# GTM Finance

**Company:** [Cursor](https://hotfix.jobs/companies/cursor)
**Location:** San Francisco, CA, New York, NY
**Role:** Revenue Operations
**Experience:** 5+ years
**Skills:** Financial Modeling, headcount planning, sales productivity, P&L Management, Revenue Operations, Sales Forecasting, business partnering, dashboard reporting, ramp curves, efficiency frameworks
**Posted:** 2026-07-23

> Finance business partner to Sales leadership, owning headcount planning, capacity/productivity models, monthly dashboards, and the consolidated Sales P&L for a high-growth AI coding tool company. Requires experience leading Sales org planning and modeling at scale.

## Job Description

## Responsibilities
- Lead headcount planning across the Sales org – partner with Sales leadership and Recruiting on hiring plans, pacing, and OpEx tradeoffs, and serve as their primary finance thought partner on investment decisions.
- Own Sales capacity, productivity, and efficiency models – and bring an independent perspective on how we size and deploy the Sales org.
- Own monthly reporting and leadership dashboards for the Sales org – build the cadence and materials that Sales and Finance leadership use to run the business, with insights that drive action.
- Own the consolidated Sales-Led P&L, connecting headcount and spend to bookings, ARR, revenue, and margin outcomes.

## Requirements
- Led Sales headcount planning, capacity, and productivity at a high-growth company, partnering directly with senior Sales leaders.
- Built capacity, productivity, and efficiency models that the business actually used to plan and forecast, and can balance accuracy, simplicity, and usability for all audiences.
- Strong perspective on Sales org design, ramp curves, productivity benchmarks, and spend efficiency, and used them to drive real resource allocation decisions – including in cases where historicals are messy or unit economics are unconventional.
- Owned reporting that executives use to run the business, and partnered with Data and RevOps to ship the underlying metrics and data models.
- High-slope and high-agency – can take complex, ambiguous problems and bring structure, clarity, and a clear recommendation.

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