Remote QA Specialist: AI Facial Data & Workflows

AuraOne, Inc.; United States; Remote; USD 28,000 - 55,000; Part time

Job summary

AuraOne, Inc. seeks an AI Facial Data Collection Contributor referrals specialist operations contractor to join a remote workflow evaluation track. You will review outputs, grade policy adherence, and document the next steps so the modeling team can improve training data.

Remote-US eligible; independent contractor with at least 10 hours weekly. You will apply multi-page rubrics, flag gaps, and provide clear written reasoning to support escalation and quality control across AI facial data

Qualifications

• Experience working on ai facial data collection referrals on real teams.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning naming the policy or workflow used.
• Strong attention to detail and ability to flag prompt issues.
• Reliable asynchronous availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current workflows, playbooks, and firm policy for referrals 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

AI Facial Data Collection Contributor

AI model evaluation

Job description

AuraOne, Inc. seeks an AI Facial Data Collection Contributor referrals specialist operations contractor to join a remote workflow evaluation track. You will review outputs, grade policy adherence, and document the next steps so the modeling team can improve training data.

Remote-US eligible; independent contractor with at least 10 hours weekly. You will apply multi-page rubrics, flag gaps, and provide clear written reasoning to support escalation and quality control across AI facial data