Jobber
Director, Software Engineering (AI Workflows & Ecosystem)
2d ago
DirectorRemoteaisoftware engineering
Leadership role to drive AI-powered workflows and ecosystem development in software engineering.
About the company
- Job by job, we’re transforming the way service is delivered. Your lawn care provider, home cleaning service, plumber or painter could use Jobber to better connect with their customers, save time in the office, invoice faster, and get paid! We’re bringing tens of thousands of people together with technology to deliver billions of dollars a year in services to happy customers. Jobber exists to help make these small businesses successful, and when they’re successful we all win!
Conditions
- At Jobber, we also believe that compensation should be transparent, fair, and supportive of your experience and growth. This role has a minimum annual salary of $221,800 CAD, a midpoint of $261,000 CAD, and a maximum salary of $300,100 CAD, designed to reflect the progression from learning the ropes to truly excelling.
- We design our compensation to reflect each new hire’s skills, experience, and the complexity of the role, ensuring a fair and competitive salary. Our range is intentionally broad to support growth and long-term impact, with fully established hires typically starting around the midpoint. The higher end of the range is reserved for those who have demonstrated deep expertise and lasting contributions, while offers below the midpoint reflect strong potential with room to develop. This approach ensures that compensation aligns with both an individual’s current capabilities and their opportunity for future growth.
- We believe in transparency and open conversations about compensation. If you have any questions about our approach, we’re happy to discuss them throughout the hiring process!
Other
- 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:
- 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.