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.