Infrastructure Reviewer, Member of Technical Staff (Remote)

AuraOne, Inc.; United States; Remote; USD 55,000 - 103,000; Full time

Job summary

AuraOne, Inc. is seeking a Member of Technical Staff, Infrastructure specialist operations contractor to review AI outputs and ensure alignment with workflows, policies, and stakeholder needs.

This role focuses on grading, documenting next steps, and flagging risks. You will work remotely from the US, applying rubrics to long batches, maintaining inter-rater calibration scores, and interfacing with modeling teams to improve training data.

Qualifications

• Direct working experience in member of technical staff infrastructure specialist operations on real teams.
• 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 member of technical staff infrastructure specialist operations workflows, playbooks, and firm policy for Member of Technical Staff, Infrastructure 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

Job description

AuraOne, Inc. is seeking a Member of Technical Staff, Infrastructure specialist operations contractor to review AI outputs and ensure alignment with workflows, policies, and stakeholder needs.

This role focuses on grading, documenting next steps, and flagging risks. You will work remotely from the US, applying rubrics to long batches, maintaining inter-rater calibration scores, and interfacing with modeling teams to improve training data.