Social Media Expert - AI 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 Social Media 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 social media, 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. Social Media scope Social inbox management, campaign and publishing execution, share-of-voice analysis, and the response judgment calls that come with an owned brand channel. Who we are looking for Hands-on practitioner experience running brand social — you have owned the account and the inbox, not only managed people who do it Fluent in a social management platform such as Sprout Social, and comfortable reading native platform analytics 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. #J-18808-Ljbffr Obsidian