Remote AI Facial Data QA Specialist

AuraOne; United States; On-site; USD 34,000 - 55,000; Full time

Job summary

AuraOne is seeking an AI Facial Data Collection Associate for a remote contractor role. You will review AI outputs, assess workflow alignment, and judge tone and escalation logic against firm policies. The position requires reliable async availability of at least 10 hours per week.

Join a research-oriented team that emphasizes precise rubric-based evaluations, documentation of next steps, and collaboration with the modeling group to improve data collection processes.

Qualifications

• Direct working experience in ai facial data collection 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 ai facial data collection specialist operations workflows, playbooks, and firm policy for AI Facial Data Collection Associate 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 specialist

AI model evaluation

Job description

AuraOne is seeking an AI Facial Data Collection Associate for a remote contractor role. You will review AI outputs, assess workflow alignment, and judge tone and escalation logic against firm policies. The position requires reliable async availability of at least 10 hours per week.

Join a research-oriented team that emphasizes precise rubric-based evaluations, documentation of next steps, and collaboration with the modeling group to improve data collection processes.