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Campfire

AI GTM Engineer

San FranciscofulltimemidAdded today

About this role

Campfire seeks an AI GTM Engineer to design and operationalize revenue systems that empower Sales and Marketing teams. You'll own the full lifecycle of GTM workflows, integrations, and data infrastructure—bridging technical execution with business strategy in a fast-paced startup.

What you'll do

  • Design and build core GTM workflows, automations, and systems including lead-to-opportunity pipelines and lead routing
  • Maintain and integrate across the revenue tech stack (CRM, marketing automation, billing, analytics) ensuring data integrity and consistency
  • Translate business requirements from Sales and Marketing teams into technical specifications and implementation roadmaps
  • Develop dashboards, metrics, and alerts to provide GTM teams with self-service visibility into performance
  • Identify process inefficiencies and lead improvement initiatives from pilot through broad-scale rollout
  • Partner with GTM leadership to surface gaps and engineer scalable solutions with cross-functional adoption

What they're looking for

  • Salesforce or HubSpot CRM administration and customization
  • RevOps or Sales Operations processes and best practices
  • Marketing automation tools and workflow design
  • Data integrity and quality management across systems
  • Business process mapping and improvement
  • API integrations and automation tools (Zapier, Make, etc.)
  • SQL or data analysis for dashboard and metrics development
  • Stakeholder communication and requirement gathering
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Campfire

Campfire builds accounting software designed for startups and mid-market tech companies, with a focus on automating financial workflows through intelligent features. The company is hiring AI engineers and full-stack engineers to develop and deploy machine learning solutions and modern platform capabilities in their San Francisco office.

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Likely interview questions

  • Tell us about a revenue system or GTM workflow you designed from scratch—what was the impact?
  • How do you approach ensuring data quality and consistency across multiple tools in a revenue stack?
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