Global Mobility QA Specialist (Remote)

AI Trainer Jobs; United States; Remote; USD 28,000 - 69,000; Full time

Benefits offered by this job

Contractor role

Remote work (US-eligible)

Job summary

AuraOne is seeking a Global Mobility Expert for a remote review track evaluating AI outputs across global mobility operations. 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.

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.

Qualifications

• Experience reviewing AI outputs against established workflows and policies.
• Ability to apply structured rubrics to assess tone, escalation logic and stakeholder fit.
• Clear written reasoning naming the policy or workflow applied.

Responsibilities

• Review AI outputs against global mobility operations 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 treatments against published playbooks or firm guidance.

Skills

Operational review

Policy adherence

Workflow judgment

Stakeholder communication

Global Mobility specialist operations

AI model training and evaluation

Business continuity planning

Crisis management

Cross-functional collaboration

Data synthesis

Global mobility management

Incident response

Tools

AI-assisted tooling

Job description

AuraOne is seeking a Global Mobility Expert for a remote review track evaluating AI outputs across global mobility operations. 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.

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.