An application made for this job — a tailored resume and cover letter that speak straight to the posting.
Mercor is building high-fidelity simulated work environments to evaluate AI agents on real marketing work in San Francisco, CA. You will design Paid Growth briefs, artifacts, and judgement criteria that reflect real-world tasks.
You should have hands-on paid acquisition experience, be fluent with Google Ads, Meta Ads, and GA4, and be able to explain why a decision is right. Prior work on AI training environments or RL simulations is strongly preferred.
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.
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.
Campaign performance diagnosis, budget allocation, funnel and pipeline diagnosis, lead recovery, account-based marketing, and web and pricing journey optimization.
Applicants complete a short multiple-choice knowledge screener specific to this sub-domain before review.