Gtm AI Engineer

Superhuman Superhuman · Consumer · United States · Remote · Managed

GTM AI Engineer to own the design and execution of AI-driven workflows across Superhuman's go-to-market stack, translating business needs into automations that improve sales processes. This role involves shipping production AI workflows, configuring AI infrastructure, managing AI connectors, and partnering with stakeholders to define and build AI solutions.

What you'd actually do

  1. Architect end-to-end AI workflows for GTM use cases like prospecting and deal execution, defining data flows, system integrations, and automation logic
  2. Ship 5 - 10 production AI workflows per quarter, from initial scoping through to live deployment and any subsequent iterations
  3. Configure MCPs and AI infrastructure and tooling; determine where data needs to come from and how it gets delivered to the right people or systems
  4. Manage Superhuman's MCP and AI connector library available to the GTM org, ensuring the tooling is discoverable, maintained, and actively adopted
  5. Break down high-level business goals (e.g. "AI-inspect our pipeline") into concrete technical requirements and build against them

Skills

Required

  • 5+ years in GTM Systems, Revenue Operations, Sales Operations, or a similar role in high-growth SaaS environments
  • Hands-on experience building AI-powered workflows, automations, or agents in a business context
  • Extensive experience with Clay, building multi-step workflows
  • Proficiency with AI tools like Claude, Gemini or Perplexity for workflow automation
  • Extensive experience with MCP (Model Context Protocol) configuration and AI agent frameworks
  • Solid understanding of how enterprise GTM tools connect together
  • Comfortable working from ambiguity to delivery

Nice to have

  • prompt design
  • tool use
  • agentic task orchestration
  • Salesforce
  • Gong
  • Outreach
  • Zoominfo
  • DealHub

What the JD emphasized

  • Hands-on experience building AI-powered workflows, automations, or agents in a business context (not just prompt engineering)
  • Extensive experience with Clay, building multi-step workflows that pull data from multiple sources and take action
  • Extensive experience with MCP (Model Context Protocol) configuration and AI agent frameworks
  • Comfortable working from ambiguity to delivery; you can take a rough idea and ship something real in weeks, not quarters

Other signals

  • AI-driven workflows
  • AI-powered revenue engine
  • AI-first world
  • production AI workflows
  • AI agent frameworks