Engineering Manager
LovableVisit Lovable website
Full-stack AI development platform for building, iterating on, and deploying web applications with natural-language prompts.
Maintainer signals as of 9/2/2026
Stockholm, Sweden
About Lovable
Lovable enables individuals and teams to create production-grade web applications using natural language. It generates frontend, backend, databases, authentication, integrations, and editable code, supporting the lifecycle from prototyping through deployment.
Skills
About the Role
Lead Lovable’s engineering organization across product, platform, and applied AI. Own the roadmap, operating model, technical direction, engineering practices, and business outcomes while remaining hands-on enough to lead difficult technical work.
Requirements
- 15+ years of engineering experience.
- 6+ years of people management experience at a pre-IPO startup, scale-up, or frontier technology company.
- At least 2 years of managing managers.
- Experience leading an engineering organization of 30+ people.
- Strong individual-contributor engineering track record.
- Ability to work hands-on as a Tech Lead, including designing and shipping changes, running incidents, and reviewing code.
- Experience working with humans and AI agents on the same team.
- Technical credibility in full-stack product, platform and developer experience, or applied AI.
- Ability to work across design, product, and go-to-market and reason about unit economics, product strategy, GTM, and customer outcomes.
- Track record of hiring senior engineers, developing managers, and establishing career growth and performance practices.
- Based in Stockholm or London, or ready to relocate.
- Ability to work on-site five days per week.
- Submit the application in English.
Responsibilities
- Lead the product, platform, and applied AI engineering organization.
- Own the engineering roadmap and business outcomes.
- Set technical direction through cross-team design documents and architectural decisions.
- Maintain a high bar for code, design, incidents, and shipped work.
- Shape engineering culture, hiring practices, deployment practices, and norms.
- Ship production work quickly and bring experiments into reality.
- Build the operating model for humans and AI agents working side by side.
- Partner with executive leadership on company-wide decisions and translate them into engineering work.
