Head of Gtm Engineering & Systems

Fireworks AI Fireworks AI · Data AI · San Mateo, CA · Go To Market

Head of GTM Engineering & Systems role focused on owning and scaling the GTM tech stack, including building agentic AI workflows for customer-facing teams. This role involves architecting foundational systems, developing a new quoting/lead-to-revenue process, and leading a growing engineering team. The primary focus is on delivering production-ready AI solutions to enhance GTM operations.

What you'd actually do

  1. Own every tool the GTM organization runs on: Salesforce architecture, enrichment layer (Clay, Harmonic, Sumble), routing (LeanData), engagement (Gong, Lemlist, Granola), and the integrations between them.
  2. You'll build a durable quoting and lead-to-revenue architecture from scratch, whether that's native Salesforce CPQ, a purpose-built tool, or a custom solution.
  3. You'll build the workflows that change that: AI-assisted account research, rep-facing deal intelligence, automated pipeline hygiene, manager insights.
  4. You're inheriting two strong engineers. You'll grow from there: defining hiring profiles, setting technical standards, building career paths, and maintaining a culture where high-quality, fast-shipping work is the norm.

Skills

Required

  • 10+ years in GTM Engineering, Revenue Operations, or Sales Operations at B2B companies
  • 5+ years of people management experience
  • Architected in Salesforce at real depth: data model, process automation, integrations, governance
  • Driven real world sales rep productivity enhancements measured in $s not time saved
  • Built or rebuilt a quoting or CPQ workflow at meaningful scale
  • Led a team and set technical direction
  • Track record of building automation that sales reps actually use

Nice to have

  • Consumption business model experience
  • Has shipped agentic or AI-native GTM workflows in production (not pilots)
  • Experience scaling GTM systems through a high-growth phase (Series B to D)
  • Familiarity with usage-based or consumption pricing models
  • Worked at an AI/ML infrastructure, developer tools, or API-first company

What the JD emphasized

  • architectural decisions
  • ship fast
  • raise the bar
  • build a durable quoting and lead-to-revenue architecture from scratch
  • agentic AI from concept to production
  • workflows the team can't imagine working without, not demos
  • building automation that sales reps actually use
  • shipped agentic or AI-native GTM workflows in production (not pilots)

Other signals

  • building agentic AI from concept to production
  • architectural decisions for GTM tech stack
  • building a durable quoting and lead-to-revenue architecture from scratch
  • building workflows that change how GTM teams leverage AI
  • growing and leading a GTM Engineering team