Founding GTM Lead for Enterprise AI Fleet Ops

Proaction, Inc.

San Francisco (CA)

On-site

USD 150,000 - 230,000

Full time

14 days+
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Job summary

Proaction, Inc. is seeking a Founding GTM Lead to build and operate Proaction's first repeatable go-to-market engine for the enterprise fleet market.

You will own the loop from target-account selection through engagement, qualification, CRM readiness, and learning, working directly with the founders to shape the early GTM motion and prove its value before scaling. Success will be measured by pipeline quality, priority-account progression, and rapid learning velocity as you design experiments,

Qualifications

  • Demonstrated end-to-end ownership of a B2B GTM motion from hypothesis through launch and iteration.
  • Ability to map buying groups and craft tailored value propositions.
  • Hands-on operator capable of building and measuring the process with limited resourcing.

Responsibilities

  • Learn from customers and prospects to focus target accounts and engagement.
  • Map buying groups, stakeholders, and actions for priority accounts.
  • Design and execute engagement across networks with tailored messaging and content.
  • Turn customer evidence into ROI narratives and messaging for enterprise buyers.
  • Establish CRM definitions, attribution, and dashboards for progress tracking.
  • Run growth experiments to optimize GTM effectiveness.
  • Lead weekly reviews of pipeline and account progression.

Skills

End-to-end GTM ownership
Enterprise account strategy
AI-native execution
Strong communication

Tools

HubSpot

Job description

Proaction, Inc. is seeking a Founding GTM Lead to build and operate Proaction's first repeatable go-to-market engine for the enterprise fleet market.

You will own the loop from target-account selection through engagement, qualification, CRM readiness, and learning, working directly with the founders to shape the early GTM motion and prove its value before scaling. Success will be measured by pipeline quality, priority-account progression, and rapid learning velocity as you design experiments,

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