Remote Voice Data Evaluation Specialist

AuraOne, Inc.; United States; A distancia; USD 34.000 - 83.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Linguistics background

Inter-rater calibration experience

Domain expertise in safety review

Descripción de la vacante

AuraOne, Inc. is seeking a remote Voice Data Contributor evaluator to review prompts and responses against our quality rubric. You will label edge cases, assign severity tags, and provide structured feedback to help retrain the model.

Responsibilities include comparing paired model outputs, writing rationales, and flagging hallucinations or unsafe content. You will calibrate against gold-standard examples and document recurring issues for future training cycles.

Formación

• Prior evaluation, annotation, or human-rater experience on voice data contributor evaluation or adjacent content.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning naming the issue and rubric clause.
• Strong attention to detail and ability to flag when a prompt itself is the problem.
• Reliable async availability for at least 10 hours per week.

Responsabilidades

• Evaluate voice data contributor evaluation model outputs against a versioned rubric and assign severity tags for Voice Data Contributor 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.

Conocimientos

Evaluation experience

Annotation

Clear written reasoning

Attention to detail

Async availability

Descripción del empleo

AuraOne, Inc. is seeking a remote Voice Data Contributor evaluator to review prompts and responses against our quality rubric. You will label edge cases, assign severity tags, and provide structured feedback to help retrain the model.

Responsibilities include comparing paired model outputs, writing rationales, and flagging hallucinations or unsafe content. You will calibrate against gold-standard examples and document recurring issues for future training cycles.