Data Annotator

AI Trainer Jobs; United States; Remote; USD 21,000 - 34,000; Full time

Job summary

AI Trainer Jobs is seeking a Data Annotator for a remote evaluation track. Review prompts and responses against AuraOne's quality rubric, compare outputs, and provide structured feedback the modeling team can use to retrain.

You will help turn evaluations into auditable labels, rationales, and regression cases. Responsibilities include tagging edge cases, labeling hallucinations, and calibrating scores against gold standards each week, with at least 10 hours per week of async availability for a

Qualifications

• Prior evaluation, annotation, or human-rater experience.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and rubric clause.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate data annotator evaluation outputs against a versioned rubric and assign severity tags for Data Annotator 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.

Job description

Data Annotator is a remote evaluation track for reviewing data annotator 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.

Category: Frontier Model Evaluation · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

Data Annotator is a remote evaluation track for reviewing data annotator evaluation prompts and responses against AuraOne's quality rubric.

Role details

About the role

Data Annotator is a remote evaluation track for reviewing data annotator 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.
AI data reviewers help turn data annotator evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review frontier model outputs. Judge benchmark failures and calibrate other evaluators.

Responsibilities

• Evaluate data annotator evaluation model outputs against a versioned rubric and assign severity tags for Data Annotator 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.

What you should bring

• Prior evaluation, annotation, or human-rater experience on data annotator evaluation or adjacent content for Data Annotator 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 data annotator 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.

Useful experience

• 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.

Compensation and schedule

Hourly rate confirmed after the interview process.
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Data Annotator evaluation

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.
- 01 Confirm profile and eligibility details.
- 02 Attach this role deliberately.
- 03 Receive a human review decision or follow-up.
Placement timing depends on program demand and reviewer confirmation.