AI Marketing Enablement Manager, Jobber

• Hands-on experience using AI to materially improve marketing workflows or business processes, with experience on a working prototype on a real workflow
• Experience building AI-powered tools, agents, automations, custom assistants, integrations, or agent workflows, and connecting them to real data sources
• Strong understanding of marketing work, ideally across demand generation, lifecycle, paid acquisition, SEO/GEO/AEO, content, marketing operations, analytics, or growth
• The ability to quickly learn how marketers work today and identify where AI creates meaningful leverage, and the judgment to know what should and shouldn’t be automated
• A frontier mindset with the curiosity to spot where AI can do something genuinely new, not just faster, and the drive to prototype it, test it, and prove whether it holds
• A quality mindset: comfort defining evals, testing outputs, and designing the human-in-the-loop checkpoints that make AI trustworthy in production
• Strong coaching and enablement skills, with the ability to teach technical concepts to non-technical audiences and adapt to different levels of AI fluency
• Strong systems thinking with turning one-off improvements into scalable, documented patterns
• Comfort operating in ambiguity in a fast-moving field, and a habit of keeping current as models, tools, and best practices change
• Experience managing multiple stakeholders, workstreams, and priorities at once
• Excellent written and verbal communication skills

Desirable

• The Demand Generation team helps Jobber reach and engage small home service businesses through scalable marketing programs
• We partner closely with Brand, Product Marketing, Marketing Operations, Sales, Data, and Product teams to create campaigns and experiences that drive awareness, pipeline, conversion, and customer growth
• This role will sit within Demand Generation and act as a force multiplier for the marketing organization
• You’ll help marketers turn ideas into execution faster, reduce repetitive work, and develop new AI-enabled ways of planning, launching, analyzing, and optimizing campaigns
• This is a builder-and-coach role and also an ownership role. You’ll inherit an in-flight AI enablement program, which is a library of tools, agents, MCP integrations, prompts, evals, documentation, and a partially enabled team
• Ou’ll become the internal owner who extends it, keeps it reliable, and drives adoption across Demand Generation and adjacent marketing teams
• You’ll partner with marketers across campaign strategy, lifecycle, paid acquisition, SEO, GEO/AEO, content, CRO, marketing operations, and analytics
• You’ll create practical solutions yourself, then help marketers adopt, adapt, and eventually build on them independently
• Because this field moves fast, part of the job is keeping our approach current as models, tools, and best practices evolve. You’re defining a capability, not maintaining a fixed toolset
• And the ambition goes beyond doing today’s work faster. We want AI to expand what our marketing team can do at all
• You will be surfacing opportunities we can’t currently see and taking on work that simply isn’t practical by hand, like analyzing every creative we’ve run to learn what truly drives performance
• Efficiency is the floor; new capability is the ceiling. A core part of this role is finding those frontier use cases and turning the ones that prove valuable into things the team can rely on
• Take ownership of the assets built during our external engagement: tools, agents, MCP servers, prompts, evals, and documentation, and make sure they remain reliable, understood, and extensible after the vendor rolls off
• Own and advance the AI maturity model already in motion, moving marketers from first useful wins to regular integration to self-sufficiency, and reporting progress to leadership
• Own the marketing AI roadmap: prioritize what to build next, decide what to sunset, and set the standards and patterns the team builds against
• Build AI-powered tools, agents, automations, and skills tailored to real marketing workflows - e.g. campaign planning, audience research, content development, channel execution, reporting, experimentation, and optimization
• Build and maintain integrations that connect AI to live data - e.g., MCP servers linking ad platforms, analytics, and CRM. This is so marketers can work against real data in natural language instead of exporting and reconciling by hand
• Establish quality and reliability practices: evals, regression tests, and human-in-the-loop review so tools can be trusted against real campaigns and real data, and so they don’t silently break when models or platforms change
• Use AI to uncover opportunities the team can’t currently see: patterns across campaigns, audiences, creative, channels, and content that would be impractical to surface by hand
• Take on analysis at a scale humans can’t - e.g. evaluating hundreds of creative variants, ad transcripts, landing pages, or search terms at once to extract what’s working and why, then feeding that back into strategy
• Prototype net-new capabilities, not just faster versions of existing work: doing things we couldn’t do before, testing whether they hold, and turning the ones that prove valuable into durable, repeatable tools
• Embed with marketers to deeply understand their workflows, goals, deliverables, tools, and pain points, and identify the highest-leverage places AI creates real leverage
• Coach marketers through a practical adoption journey, from first win to regular use to building their own workflows
• Recognize patterns across individual use cases and turn one-offs into reusable playbooks, templates, and tools, documented in version control so any marketer can read, run, and extend them
• Share wins and examples to build momentum, confidence, and excitement around AI-enabled marketing work
• Extend the model beyond Demand Gen into the broader marketing organization as the capability matures
• Partner with Data, Security, and IT to keep solutions scalable, reliable, and responsible with approved AI providers, sound data handling, appropriate human review, and clear accountability for AI outputs and actions
• Track impact with the number of workflows transformed, time saved (measured before/after), adoption, maturity progression, and library reuse
• To support this, the role is open to candidates based in one of our hub cities: Edmonton, Toronto, Vancouver, or Kitchener-Waterloo