Lovable

Lovable

GTM Engineering Lead

Boston

Work authorization requiredDetected 21 days ago
JavaScriptTypeScriptPythonMachine LearningData EngineeringLLMsAI OrchestrationCRM

About the role

  • We're building AI-powered GTM systems from scratch.
  • You'll lead the design and execution of the agents, automations, and workflows that help Lovable win deals, onboard customers faster, and scale.
  • You'll own our agent orchestration strategy, the data pipelines that power it, and our PLG motion in enterprise end-to-end.

Responsibilities

  • Own PLG strategy and execution for enterprise: design the full motion from activation to expansion, and build the systems that make it run without manual intervention.
  • Build and scale the agent layer that powers our GTM: from outreach personalization to deal routing, you'll architect what we use to win.
  • Own the data infrastructure that makes all of it intelligent: product signals, enrichment pipelines, CRM sync, the works.
  • Build in Lovable: you'll design agents and apps directly in the product, not just describe what you want someone else to build.
  • BS/MS in Computer Science, AI/ML, or a related field, or equivalent depth demonstrated through professional experience building production AI systems.

Requirements

  • Proven track record leading a GTM engineering or growth engineering function at a high-growth SaaS company, with direct reports.
  • Hands-on experience architecting and owning agent orchestration systems end-to-end, from framework selection to production deployment and iteration.
  • Deep experience designing and owning data pipelines that feed GTM systems, including product usage signals, enrichment flows, and CRM sync.
  • Proficiency in Python and/or JavaScript/TypeScript, and genuine obsession with the LLM stack.
  • Deep experience with Lovable.

Company info

  • What we're looking for

Visa & Work Authorization

  • At this time we are unable to support work authorization in the US.

This listing is sourced directly from Lovable's careers page and normalized into a canonical job model.