Jobber exists to help people in small businesses be successful. We work with small home service businesses, like your local plumbers, painters, and landscapers, to transform the way service is delivered through technology. With Jobber, they can quote, schedule, invoice, and collect payments from their customers while providing an easy and professional customer experience. Running a small business today isn’t like it used to be—the way we consume and deliver service is changing rapidly, technology is evolving, and customers expect more. That’s why we put the power and flexibility in their hands to run their businesses how, where, and when they want!
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
Decide what to do next
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:
“What should I do next?”
“Why didn’t this job convert?”
“Who should I follow up with today?”
And eventually:
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)
You are responsible for:
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
This includes:
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
Not “we shipped AI features.”
Instead:
The system proactively recommends and takes actions
Teams build on shared AI primitives, not reinventing them
AI output is reliable, measurable, and improving over time
Engineers trust the system, and move faster because of it
Customers feel like the product is working for them, not just responding
We are not looking for:
Someone who rolled out Copilot internally
Someone who used LLM APIs for features
Someone adjacent to AI
We are looking for someone who has:
Built real systems where AI makes decisions and takes actions in production.
That means experience with:
Agent orchestration (not just prompts)
Tool use and workflow execution
Evaluation (offline + online)
Observability and failure handling
Guardrails and safety in real systems
Tradeoffs between autonomy vs. control
You don’t need to code daily, but you must be able to reason at the system level.
Define how AI should work across Jobber, not just within a team
Build and evolve a multi-team org to execute on that vision
Make tradeoffs between speed, quality, and safety
Push teams beyond feature thinking into system thinking
Challenge assumptions, including leadership’s
Drive adoption across engineering, product, and the company
You’ve led orgs through complexity, not just growth.
Managed managers across multiple teams
Built organizations that scale (not just teams that ship)
Driven cross-org alignment in ambiguous spaces
You think in systems, not features.
You understand how user workflows connect end-to-end
You’ve partnered deeply with Product and Design
You care about customer outcomes, not just technical output
You’ve built or led production LLM/agentic systems.
You understand what actually works (and what doesn’t)
You’ve seen systems fail and improved them
You have opinions about evaluation, reliability, and safety
You can move fast without breaking everything.
You’ve balanced shipping vs infrastructure vs tech debt
You know when to iterate and when to redesign
This is not “AI theatre.”
We already have:
AI Receptionist (live, handling real cust