Remote ML Operations Reviewer & Policy QA

AuraOne; United States; On-site; USD 82,656 - 123,984; Full time

Job summary

AuraOne seeks a Machine Learning Expert reviewer to evaluate AI outputs against internal workflows, rubrics, and policy, ensuring accuracy and proper escalation. The contractor will document next steps to guide model training and audit adherence.

Remote, US-eligible, contractor-based role. Responsibilities include grading tone, escalation logic, and stakeholder alignment while flagging gaps and maintaining calibration scores across reviews.

Qualifications

• Direct working experience in machine learning specialist operations on real teams for Machine Learning Expert 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 machine learning specialist operations workflows, playbooks, and firm policy for Machine Learning Expert 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

ML operations

Job description

AuraOne seeks a Machine Learning Expert reviewer to evaluate AI outputs against internal workflows, rubrics, and policy, ensuring accuracy and proper escalation. The contractor will document next steps to guide model training and audit adherence.

Remote, US-eligible, contractor-based role. Responsibilities include grading tone, escalation logic, and stakeholder alignment while flagging gaps and maintaining calibration scores across reviews.