Director, Software Engineering (AI Workflows & Ecosystem)
Role Overview
Jobber builds software for small home-service businesses to quote, schedule, invoice, and collect payments.
The Director owns the product-wide AI system layer, evolving fragmented AI features into connected, proactive workflows and business operations.
The scope includes AI foundations, Copilot, automations, platform experience and marketplace, and emerging voice, messaging, and cross-product intelligence surfaces.
Key Skills for This Role
Full Job Posting
Role and opportunity
Jobber builds software for small home-service businesses to quote, schedule, invoice, and collect payments.
The Director owns the product-wide AI system layer, evolving fragmented AI features into connected, proactive workflows and business operations.
The scope includes AI foundations, Copilot, automations, platform experience and marketplace, and emerging voice, messaging, and cross-product intelligence surfaces.
System ownership
- Define how system decisions are made, how context moves across workflows, when actions are triggered, and how AI effectiveness is evaluated.
- Build agentic workflows that reason, decide, act, and evaluate, using cross-product context such as jobs, customers, payments, and communication.
- Ensure reliability, safety, and failure handling, and improve developer experience for teams building on AI systems.
- Define the AI approach across Jobber and move teams beyond isolated features toward shared system thinking.
- Make tradeoffs among speed, quality, safety, and autonomy; challenge assumptions, including leadership’s.
- Drive adoption across engineering, product, and the company.
What the role requires
- Built production systems where AI makes decisions and takes actions; experience limited to internal Copilot rollouts, LLM API features, or adjacent AI work is not the target.
- Experience with agent orchestration, tool use, workflow execution, and offline and online evaluation.
- Experience with observability, failure handling, guardrails, and safety in real systems.
- Able to reason at the system level; coding daily is not required.
- Able to make tradeoffs between autonomy and control and between speed, quality, and safety.
Team and success measures
The organization is described as approximately 30 engineers across 4–6 teams, with 4–6 engineering managers or senior engineering managers reporting to the Director.
The role partners closely with Product, Design, and Data.
Success means proactive recommendations and actions, shared AI primitives, measurable and improving reliability, faster engineering, and a product that helps customers run their businesses.
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