Remote Cantonese Evaluation Specialist (Contractor)

AuraOne; United States; On-site; USD 34,000 - 55,000; Full time

Job summary

AuraOne is seeking a remote Cantonese Transcription Expert contractor to evaluate model outputs against a versioned rubric, tag severity, and provide structured feedback. You will compare responses, label edge cases, and document recurring issues to improve training datasets.

Ideal candidates have prior Cantonese evaluation experience, can apply rubrics consistently across large batches, and provide clear written rationales for rubric choices.

Qualifications

• Prior evaluation, annotation, or human-rater experience on cantonese generalist evaluation or adjacent content.
• 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.

Responsibilities

• Evaluate cantonese generalist evaluation model outputs against a versioned rubric and assign severity tags for Cantonese Transcription Expert assignments.
• Compare paired responses and pick the stronger answer with 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.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Cantonese generalist evaluation

Job description

AuraOne is seeking a remote Cantonese Transcription Expert contractor to evaluate model outputs against a versioned rubric, tag severity, and provide structured feedback. You will compare responses, label edge cases, and document recurring issues to improve training datasets.

Ideal candidates have prior Cantonese evaluation experience, can apply rubrics consistently across large batches, and provide clear written rationales for rubric choices.