Data-Video Generalist (LATAM).

Data-Video Generalist (LATAM) is a remote evaluation track for reviewing data video generalist evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.

Data Video Generalist evaluation evaluation is where AI quality gets decided. AuraOne treats reviewer judgment like instrumented training data — every label is paired with rationale so the team can debug the rubric, not just the model.

Review model outputs, label edge cases, and improve training quality across high-volume AI workflows.

• ↳Evaluate data video generalist evaluation model outputs against a versioned rubric and assign severity tags for Data-Video Generalist (LATAM) assignments.
• ↳Compare paired responses and pick the stronger answer with a written rationale.
• ↳Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• ↳Capture ambiguous prompts and route them back to the program team for rubric updates.
• ↳Maintain reviewer-quality scores by calibrating against gold-standard examples each week.
• ↳Document recurring failure modes so the modeling team can target them in the next training run.

• ↳Prior evaluation, annotation, or human-rater experience on data video generalist evaluation or adjacent content for Data-Video Generalist (LATAM) work.
• ↳Comfort applying multi-page rubrics consistently across long batches.
• ↳Clear written reasoning that names the issue and the rubric clause 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.

Compensation reviewed during specialist intake

Expected schedule: contractor, remote specialist work with program-defined task volume and review pacing.

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Data Video Generalist evaluation
• Design

AuraOne uses a shared specialist intake to confirm track fit, review readiness, and the best queue for your profile. Applications submitted from partner job boards carry the source, role, and category on the apply URL.