AI Agent QA Specialist — Remote Operations & Policy

AuraOne; United States; Remote; USD 99,000 - 135,000; Full time

Job summary

AuraOne is seeking an experienced Everyday AI Agent Tester to focus on evaluating AI outputs within everyday agent tester operations. This remote contractor role emphasizes reviewing workflows, policy adherence, and tone, with clear next steps documented for the modeling team.

You will work asynchronously for about 10+ hours per week, grading tasks against playbooks, flagging escalation gaps, and contributing to calibration cycles to maintain reviewer quality.

Qualifications

• Direct working experience in everyday AI agent tester specialist operations on real teams.
• Clear written reasoning that names the policy or workflow being applied.
• Reliable async availability for at least 10 hours per week.
• Comfort applying multi-page rubrics consistently across long batches.

Responsibilities

• Review AI outputs against current everyday AI agent tester specialist operations workflows, playbooks, and firm policy for Everyday AI Agent Tester (Remote) 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.
• Maintain reviewer-quality scores in inter-rater calibration cycles.

Skills

Operational review

Policy adherence

Workflow judgment

Stakeholder communication

Everyday AI Agent Tester specialist...

AI model evaluation

Job description

AuraOne is seeking an experienced Everyday AI Agent Tester to focus on evaluating AI outputs within everyday agent tester operations. This remote contractor role emphasizes reviewing workflows, policy adherence, and tone, with clear next steps documented for the modeling team.

You will work asynchronously for about 10+ hours per week, grading tasks against playbooks, flagging escalation gaps, and contributing to calibration cycles to maintain reviewer quality.