Senior ML Operations Reviewer (Remote Contractor)

AuraOne, Inc.; United States; Remote; USD 85,000 - 121,000; Full time

Job summary

AuraOne, Inc. seeks a remote Machine Learning Expert (Ph.Ds/Post-Docs) to review AI outputs within dedicated specialist operations workflows.

You will assess workflow correctness, policy adherence, and stakeholder fit using a structured rubric and document next steps for model training. Key tasks include flagging operational risks, ensuring escalations are handled with the right severity, and adjudicating playbook guidance against real outputs.

Qualifications

• Direct working experience in machine learning specialist operations on real teams for Machine Learning Expert (Ph.Ds/Post-Docs) 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 (Ph.Ds/Post-Docs) 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

Machine Learning ops

AI model evaluation

Analytical thinking

Data annotation

Literacy

Presentation

Job description

AuraOne, Inc. seeks a remote Machine Learning Expert (Ph.Ds/Post-Docs) to review AI outputs within dedicated specialist operations workflows.

You will assess workflow correctness, policy adherence, and stakeholder fit using a structured rubric and document next steps for model training. Key tasks include flagging operational risks, ensuring escalations are handled with the right severity, and adjudicating playbook guidance against real outputs.