Jobber has AI in production, but not yet at its full potential.
We already have AI answering calls, drafting responses, and powering parts of our product. But today, those systems are still fragmented. Some teams are ahead. Others aren’t. Some workflows are intelligent. Others are still manual. And most importantly, the system doesn’t yet think across the product.
A service pro still has to:
•
Manually follow up on jobs
•
Piece together context across workflows
The platform doesn’t proactively help them run their business. That’s the gap.
The opportunity is to evolve Jobber from: AI-powered features → AI-powered workflows → AI-powered business operations
This role owns that shift. Not a team. Not a feature. The system.
You’re building for people who don’t have time to think about software.
•
A plumber finishing their last job at 6 pm
•
A cleaner managing 30 clients and 5 employees
•
A landscaper juggling scheduling, payments, and follow-ups
They’re not asking for “AI.” They’re asking:
•
“Why didn’t this job convert?”
•
“Who should I follow up with today?”
•
They shouldn’t have to ask at all.
The Director who succeeds here will understand:
This isn’t about building clever systems; it’s about building systems that remove thinking from already overwhelmed people.
End-to-end ownership of Jobber’s AI system layer. You’re not owning a single team. You’re owning how intelligence flows across the entire product.
•
AI Foundations (models, orchestration, evals, guardrails)
•
Copilot (user-facing intelligence layer)
•
Automations (workflow execution layer)
•
Platform Experience / Marketplace (integration + ecosystem surface)
•
Emerging surfaces (voice, messaging, cross-product intelligence)
•
How decisions get made inside the system
•
How context moves across workflows
•
How actions get triggered (and when they shouldn’t)
•
How we evaluate whether AI is actually working
•
Agentic workflows (reason → decide → act → evaluate)
•
Cross-product context (jobs, customers, payments, communication)
•
Reliability, safety, and failure modes
•
Developer experience for building on top of AI systems
•
~30 engineers across 4–6 teams
•
4–6 EMs / Sr EMs reporting into you
•
Close partnership with Product, Design, Data