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Lovable is a no-code/low-code platform enabling anyone to build software with any language, from solopreneurs to Fortune 100 teams. Millions of users leverage Lovable to transform ideas into products rapidly, with enterprise adoption accelerating.
You will lead technical enablement across Lovable's entire go-to-market organization: Sales, Customer Success, and Solutions Architecture. This is not a traditional training role—it's a systems-building position focused on eliminating enablement friction through automation, embedded knowledge, and workflow integration.
Your core responsibilities:
- Own the technical knowledge hub for GTM: demo libraries, reference architectures, competitive technical positioning, and AI use-case pattern libraries that field teams draw on in live deals.
- Raise the technical floor for Account Executives and Customer Success Managers—both new hires and tenured reps—ensuring they can articulate the art of the possible and lead AI transformation conversations with confidence.
- Leverage the Solutions Architect team as a force multiplier by extracting their expertise, systematizing it, and making it accessible to the broader field without requiring every CSM to become an SA.
- Co-own SA onboarding and ongoing development alongside the Head of SA, ensuring new SAs ramp quickly and tenured SAs have infrastructure to deepen their skills.
- Drive measurable outcomes: AE and CSM technical readiness, deal-stage SA leverage, and quality of technical narrative across the funnel.
Immediate priorities include auditing current technical knowledge assets and mapping gaps against deal losses, building the first AI use-case pattern library and reference architecture set, and designing the SA-as-knowledge-multiplier model.
You think in systems and leverage, not checklists. You've spent 7+ years in technical GTM roles (SA, SE, solutions consultant, or technical enablement), built systems to scale field learnings, and are fluent in AI use cases and enterprise AI transformation. You're technically credible enough to peer with SAs and commercially sharp enough to translate depth into field-actionable insights. Experience at PLG or AI-native companies is preferred.
