Marketing Expert , Paid Growth (AI Agent Environments)

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 Paid Growth 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 paid acquisition, 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. Paid Growth scope Campaign performance diagnosis, budget allocation, funnel and pipeline diagnosis, lead recovery, account-based marketing, and web and pricing journey optimization. Who we are looking for Hands-on practitioner experience running paid acquisition , you have owned budget and results, not only managed people who do it Fluent in the tools of the domain: Google Ads, Meta Ads, and Google Analytics 4 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.