Darum lohnt es sich
About the work
We are building high-fidelity simulated work environments used to evaluate and improve AI agents on real marketing work.
Each environment reproduces a marketing org's actual tool surface - email, storage, CRM, project management, social media management, web analytics, AEO/SEO, ads, CMS, product analytics and support - populated with realistic documents, dashboards, personas and deliberately planted problems.
You will help design and pressure-test the Content Marketing environments: the briefs, the artifacts, the judgment calls a strong practitioner would make, and the errors a weaker one would miss.
What you will do
• Design task briefs drawn from work you have actually done in content marketing, including what the task is really testing - the planted issue and the decision a strong practitioner should reach
• Specify the documents, dashboards, personas and tool states a realistic version of that task requires
• Write or review the reference answer and the criteria that separate a strong response from a plausible-but-wrong one
• Review AI agent attempts and judge them against your own standard
Tasks span the capabilities we measure: diagnosing what happened from messy or conflicting data, prioritizing and making tradeoffs, planning and executing, QA and reconciliation, triage and escalation, research and evaluation, reporting, and orchestrating multi-step work across several tools.
Content Marketing scope
Research and discovery, editorial prioritization, content production, and content QA and audit.
Who we are looking for
• Hands-on practitioner experience in content marketing - you have owned an editorial program end to end, not only managed people who do it
• Comfortable working in a CMS such as Contentful, alongside Google Analytics 4 and a research tool such as Semrush
• Prior experience building AI training environments, RL environments or simulated case studies is strongly preferred
• Rubric Academy or Rubric Bootcamp certification is strongly preferred
• Clear written reasoning - much of the value is in explaining why a decision is right
Screening
Applicants complete a short multiple-choice knowledge screener specific to this sub-domain before review.
