Director, Software Engineering (AI Workflows & Ecosystem)

Are you driven to bring people, technology, and strategy together to build impactful software? 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! THE PROBLEM YOU’D OWN 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 [... source excerpt omitted ...] ot a feature. The system. THE CUSTOMER 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. WHAT YOU’D OWN End-to-end ownership of Jobber’s AI [... source excerpt omitted ...] gered (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 Team Structure ~30 engineers across 4–6 teams 4–6 EMs / Sr EMs reporting into you Close partnership with Product, Design, Data WHAT “GOOD” LOOKS LIKE 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 Enginee