Uncertainty Calibration Failure Analysis Reviewer

AuraOne, Inc.; United States; Remote; USD 34,000 - 62,000; Full time

Job summary

AuraOne, Inc. seeks a meticulous Uncertainty Calibration Failure Analysis Reviewer for a remote contractor role. You will evaluate model outputs against a versioned rubric, assign severity tags, and provide structured feedback to retrain prompts.

Responsibilities include comparing paired responses, labeling edge cases, documenting recurring issues, and calibrating against gold-standard examples each week. Applicants should have prior annotation experience and be available asynchronously for at

Qualifications

• Prior evaluation, annotation, or human-rater experience on uncertainty calibration failure analysis 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 uncertainty calibration failure analysis evaluation model outputs against a versioned rubric and assign severity tags for Uncertainty Calibration Failure Analysis Reviewer 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.

Skills

Model evaluation

Rubric annotation

Severity tagging

Inter-rater calibration

Uncertainty calibration

Frontier evaluation

Rubric calibration

Failure analysis

Uncertainty

Calibration

Job description

Uncertainty Calibration Failure Analysis Reviewer is a remote evaluation track for reviewing uncertainty calibration failure analysis 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.

Why this role matters

AI data reviewers help turn uncertainty calibration failure analysis evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.

Responsibilities

• Evaluate uncertainty calibration failure analysis evaluation model outputs against a versioned rubric and assign severity tags for Uncertainty Calibration Failure Analysis Reviewer 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.

Qualifications

• Prior evaluation, annotation, or human-rater experience on uncertainty calibration failure analysis evaluation or adjacent content for Uncertainty Calibration Failure Analysis Reviewer 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.

Example tasks

• Compare two uncertainty calibration failure analysis evaluation model responses to the same prompt and pick the stronger one with rationale.
• Tag an unsafe response with the correct policy category and severity.
• Audit a 50-row batch for rubric consistency and report drift to the program lead.
• Propose a rubric clarification after spotting a recurring failure mode.

Nice to have

• Background in linguistics, content moderation, or trust & safety review.
• Experience with inter-rater agreement metrics and calibration cycles.
• Domain expertise that lets you spot subject-matter errors automated checks miss.

Skills

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Uncertainty Calibration Failure Analysis evaluation
• Frontier evaluation
• Rubric calibration
• Failure analysis
• Uncertainty
• Calibration

Work model

Remote — US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.

Compensation

Hourly rate confirmed after the interview process.