Remote QA Reviewer – Field Recorders Operations

AI Trainer Jobs

United States

Remote

USD 110,000 - 131,000

Full time

2 days ago
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Benefits offered by this job

Contractor role
Remote - US eligible

Job summary

Field Recorders is a remote review track for evaluating AI outputs across field recorders specialist operations workflows. Reviewers grade workflow correctness, policy adherence, and stakeholder fit; flag operational risk; and document the right next step so the modeling team can train on it.

Field Recorders specialist operations 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

Qualifications

  • Direct working experience in field recorders operations.
  • Ability to apply multi-page rubrics consistently across long batches.
  • Clear written reasoning that names the policy or workflow being applied.
  • Reliable async availability for at least 10 hours per week.

Responsibilities

  • Review AI outputs against current field recorders specialist operations workflows, playbooks, and firm policy for Field Recorders 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

Operational review
Policy adherence
Workflow judgment
Stakeholder communication
Field Recorders operations

Tools

AI-assisted workflow tooling

Job description

Field Recorders is a remote review track for evaluating AI outputs across field recorders specialist operations workflows. Reviewers grade workflow correctness, policy adherence, and stakeholder fit; flag operational risk; and document the right next step so the modeling team can train on it.

Field Recorders specialist operations 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

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