Higher Education Expert

AI Trainer Jobs; United States; Remote; USD 83,000 - 96,000; Full time

Benefits offered by this job

Remote work

US-eligible contractor

Job summary

AI Trainer Jobs is seeking a Higher Education Expert for a remote review track evaluating AI outputs across education 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.

Education review AI requires experienced operators to grade outputs as a senior peer would—checking workflow, policy, and unwritten rules that decide whether a task is completed.

Qualifications

• Experience reviewing education-related outputs using structured rubrics.
• Ability to apply policy and workflow criteria consistently across tasks.
• Clear written reasoning naming the policy or workflow applied.

Responsibilities

• Review AI outputs against education review workflows, playbooks, and firm policy.
• 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 playbook calls using firm guidance.

Skills

Education review

Rubrics adherence

Policy reasoning

Attention to detail

Async availability

Tools

AI tooling

Job description

Higher Education Expert is a remote review track for evaluating AI outputs across education review 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.

Category: Education & Learning Evaluation · Pay: $60–$70 / hr · Location: Remote — US-eligible · Contractor

Higher Education Expert is a remote review track for evaluating AI outputs across education review workflows.

About the role

Higher Education Expert is a remote review track for evaluating AI outputs across education review 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.
Education review 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 rules that decide whether a task actually gets done.
Review tutoring and assessment output. Judge it against how real learners use it.

Responsibilities

• Review AI outputs against current education review workflows, playbooks, and firm policy for Higher Education 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.

What you should bring

• Direct working experience in education review on real teams for Higher Education 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.

Example tasks

• Grade a model's response to a real education review ticket and rate workflow, tone, and escalation.
• Flag a missed escalation with the right severity tag and corrected next step.
• Adjudicate a disputed playbook call between two reviewers using firm guidance.
• Audit a 25‑row batch for rubric consistency and report drift to the program lead.

Useful experience

• Prior experience training, calibrating, or QA‑ing operations teams.
• Familiarity with AI‑assisted workflow tooling and its failure modes.
• Bilingual experience for cross‑region operations.

Compensation and schedule

$60–$70 / hr
Expected arrangement: contractor , with program‑defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Operational review
• Policy adherence
• Workflow judgment
• Stakeholder communication
• Education review