Remote AI Output QA Auditor – Domain Writing

AI Trainer Jobs

United States

Remote

USD 124,000 - 165,000

Full time

3 days ago
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Job summary

AI Trainer Jobs is seeking contractors for a remote review track evaluating AI outputs across domain writing workflows. Reviewers assess workflow correctness, policy adherence, and stakeholder fit, flag operational risk, and document next steps for the modeling team.

Domain writing AI has to fit into an actual day at work. AuraOne uses experienced operators to grade outputs the way a senior peer would — checking workflow, policy, and the unwritten rules that decide whether a task actually gets

Qualifications

  • Direct working experience in domain writing on real teams for Technical Writers work.
  • Comfort applying multi-page rubrics consistently across long batches.
  • Clear written reasoning that names the policy or workflow being applied.
  • Strong attention to detail and the ability to flag when a prompt itself is the problem.
  • Reliable async availability for at least 10 hours per week.

Responsibilities

  • Review AI outputs against current domain writing workflows, playbooks, and firm policy for Technical Writers assignments.
  • Grade tone, escalation logic, and stakeholder fit on a structured rubric.
  • Flag operational risk, missed escalations, and policy-adherence gaps with severity tags.
  • Capture the right next step so the modeling team can train on it.
  • Adjudicate disputed treatments against published playbooks or firm guidance.

Skills

Domain writing experience
Rubric application
Written reasoning
Attention to detail
Async availability

Tools

AI tooling familiarity

Job description

AI Trainer Jobs is seeking contractors for a remote review track evaluating AI outputs across domain writing workflows. Reviewers assess workflow correctness, policy adherence, and stakeholder fit, flag operational risk, and document next steps for the modeling team.

Domain writing AI has to fit into an actual day at work. AuraOne uses experienced operators to grade outputs the way a senior peer would — checking workflow, policy, and the unwritten rules that decide whether a task actually gets

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