Director of AI Systems

Jobber exists to help small businesses—like plumbers, painters, and landscapers—transform service delivery through technology. Their platform enables quoting, scheduling, invoicing, and payments while enhancing customer experiences. The role focuses on evolving Jobber’s AI from isolated features to integrated, intelligent workflows that proactively assist business owners.

THE PROBLEM

Jobber’s AI is already in production but fragmented. Some workflows are intelligent, while others remain manual. The system lacks cohesive decision-making across the product, forcing service professionals to manually follow up on jobs, piece together context, and decide next steps. The gap is shifting from AI-powered features to AI-powered workflows and ultimately AI-powered business operations.

THE CUSTOMER

Built for busy small business owners—like a plumber finishing their last job at 6 PM, a cleaner managing 30 clients and 5 employees, or a landscaper juggling scheduling, payments, and follow-ups—they don’t ask for “AI.” Instead, they need:

• “What should I do next?”
• “Why didn’t this job convert?”
• “Who should I follow up with today?”

Ideally, they shouldn’t have to ask at all. The Director must understand that this isn’t about clever systems—it’s about removing cognitive load from overwhelmed users.

WHAT YOU’D OWN

End-to-end ownership of Jobber’s AI system layer, spanning:

• AI Foundations (models, orchestration, evaluations, guardrails)
• Copilot (user-facing intelligence layer)
• Automations (workflow execution layer)
• Platform Experience / Marketplace (integration and ecosystem)
• Emerging Surfaces (voice, messaging, cross-product intelligence)

Responsibilities include:

• How decisions get made inside the system
• How context moves across workflows
• How actions get triggered (and when they shouldn’t)
• Evaluating whether AI is actually working

Key focus areas:

• Agentic workflows (reason → decide → act → evaluate)
• Cross-product context (jobs, customers, payments, communication)
• Reliability, safety, and failure modes
• Developer experience for building on AI systems

TEAM STRUCTURE

~30 engineers across 4–6 teams, with 4–6 Engineering Managers/Senior Engineering Managers reporting to you. Close collaboration with Product, Design, and Data teams.

WHAT “GOOD” LOOKS LIKE

Not just shipping AI features. Instead:

• The system proactively recommends and takes actions
• Teams build on shared AI primitives, avoiding reinvention
• AI output is reliable, measurable, and improving over time
• Engineers trust the system, accelerating their workflows
• Customers feel the product is working for them, not just responding

THE AI BAR

Not looking for:

• Someone who rolled out Copilot internally
• Someone who used LLM APIs for features
• Someone adjacent to AI

Looking for someone who has built real systems where AI makes decisions and takes actions in production. Experience required:

• 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 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 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

WHAT WE’RE LOOKING FOR

Leadership

• Managed managers across multiple teams
• Built organizations that scale (not just teams that ship)
• Driven cross-org alignment in ambiguous spaces

Product + Systems Thinking

• Understands how user workflows connect end-to-end
• Partnered deeply with Product and Design
• Focuses on customer outcomes, not just technical output

AI Depth (non-negotiable)

• Built or led production LLM/agentic systems
• Understands what actually works (and what doesn’t)
• Seen systems fail and improved them
• Has opinions about evaluation, reliability, and safety

Execution

• Balanced shipping vs. infrastructure vs. tech debt
• Knows when to iterate and when to redesign

WHY JOBBER · WHY NOW?

This is not “AI theatre.” Jobber already has:

• AI Receptionist (live, handling real customer calls)
• AI features embedded across the product
• 250,000+ businesses using the platform

What’s missing is a unified, intelligent system across the product. This role builds that.

TLDR:

Most Director roles optimize delivery. This one defines: How an entire product becomes intelligent.