Principal AI Engineer

Who you are

• A track record of building production systems at Staff+ scope: data infrastructure, retrieval, or applied AI used daily by hundreds to thousands of people who depend on it
• Hands-on depth with the modern AI stack, and at least one battle scar from a system that showed you how confidently AI can be wrong
• Pragmatic technology instincts: you reach for proven tools by default and can defend the exceptions
• Founding-engineer, consulting, or forward-deployed experience: comfortable owning outcomes inside someone else's domain
• A daily working relationship with AI tools and agents, as instruments you build with and build for
• The pragmatism to ship every few weeks, the standards to make it trustworthy anyway, and the communication skills to explain both to a CEO

What the job involves

• Reporting to the VP of Customer Experience Innovation, the Principal Software Engineer will be the founding technical seat: you'll architect and build new AI systems from zero to production, and own the hard problems that come with putting AI at the center of how a company runs — trust, permissions, security, and quality at scale
• This is a builder's role with unusual range. One week you're designing the architecture of a system nobody at Jobber has built before; the next you're embedded with a team, shipping working AI into their real workflow and making them the hero
• You'll make fast build-vs-buy calls, run POCs and kill them without sentiment, and set the technical standards that everything AI at Jobber gets held to. We're looking for someone who has shipped production AI or data systems, defaults to pragmatic technology choices, and wants founding-engineer ownership with a real company's resources behind it
• Architect and build new internal AI systems end to end, from first design to production, as the founding engineer
• Own the hard parts of trustworthy AI at company scale: retrieval quality, permissions, security, and reliability
• Work hands-on across the modern AI stack (LLMs, retrieval, agents, integrations) while keeping the foundations deliberately pragmatic
• Embed with teams across the company, working day-to-day with function leaders and their teams and weekly with executive leadership, shipping AI into real workflows and leaving behind systems and owners that outlast the visit
• Drive build-vs-buy evaluations and fast POCs, with the judgment to commit hard and kill fast
• Work directly with executive leadership: what you build informs how the company makes decisions, every day