Director, Software Engineering (AI Workflows & Ecosystem)
Jobber · USA
About this role
## Director, Software Engineering (AI Workflows & Ecosystem) ### About Jobber Jobber helps small home service businesses succeed—so plumbers, painters, landscapers, and cleaners can quote, schedule, invoice, and collect payments while delivering an easy, professional customer experience. Service delivery is changing fast, and customers expect more. Jobber is building AI-powered capabilities to meet that shift—now with an opportunity to evolve from AI features to AI-powered business operations. --- ### The Problem You’d Own Jobber has AI in production, but it’s not yet reaching its full potential. Today, AI capabilities are fragmented across teams and workflows. A service professional still has to: - Manually follow up on jobs - Piece together context across workflows - Decide what to do next **This role owns the shift** from: **AI-powered features → AI-powered workflows → AI-powered business operations** You’ll be responsible for **how the system thinks across the product**, not just a single team or feature. --- ### The Customer You’re building for people who don’t have time to think about software. They’re asking: - “What should I do next?” - “Why didn’t this job convert?” - “Who should I follow up with today?” Eventually, they shouldn’t have to ask at all. --- ### What You’d Own **End-to-end ownership of Jobber’s AI system layer**, including how intelligence flows across the entire product: **Scope** - **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’ll be 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 **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, and Data --- ### What “Good” Looks Like - Not “we shipped AI features,” but **the system proactively recommends and takes actions** - Teams build on shared AI primitives (no reinvention) - AI output is **reliable, measurable, and improving over time** - Engineers trust the system and move faster - Customers feel like the product is working for them—not just responding --- ### The AI Bar (This Role Is Different) Jobber is **not** looking for someone who: - Rolled out Copilot internally - Only used LLM APIs for features - Is adjacent to AI You should have experience building **real systems where AI makes decisions and takes actions in production**, including: - 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**. --- ### What You’ll Actually Do - 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 spee
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