Generalist for Data Annotation

AI Trainer Jobs; United States; Remote; USD 34,000 - 55,000; Part time

Job summary

AuraOne is seeking a remote Generalist for Data Annotation to evaluate prompts and responses against a quality rubric. You’ll help turn outputs into auditable labels, rationales, and regression cases for Human Data, reviewing frontier model outputs and calibrating evaluators.

Responsibilities include selecting stronger answers with rationale, tagging issues, and routing ambiguous prompts for rubric updates. Strong attention to detail and async availability are essential.

Qualifications

• Prior evaluation, annotation, or human-rater experience on generalist for data annotation evaluation or adjacent content.
• Clear written reasoning that names the issue and rubric clause.
• Strong attention to detail and ability to flag when a prompt is the problem.

Responsibilities

• Evaluate model outputs against a versioned rubric and assign severity tags for Generalist for Data Annotation assignments.
• Compare paired responses and select 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

Generalist for Data Annotation is a remote evaluation track for reviewing generalist for data annotation 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

Generalist for Data Annotation is a remote evaluation track for reviewing generalist for data annotation 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.

About the role

Generalist for Data Annotation is a remote evaluation track for reviewing generalist for data annotation evaluation prompts and responses against AuraOne's quality rubric.
AI data reviewers help turn generalist for data annotation 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 generalist for data annotation evaluation model outputs against a versioned rubric and assign severity tags for Generalist for Data Annotation 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.

Role details

Track Evaluation & annotation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Prior evaluation, annotation, or human-rater experience on generalist for data annotation evaluation or adjacent content for Generalist for Data Annotation 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.

Role signals

Example tasks

• Compare two generalist for data annotation 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
• Generalist for Data Annotation 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.