Harmlessness Ranking Preference Data Reviewer

AI Trainer Jobs; United States; Remote; USD 28,000 - 83,000; Full time

Benefits offered by this job

Remote work

Hourly contractor role

Job summary

AI Trainer Jobs is seeking a remote Harmlessness Ranking Preference Data Reviewer. You will compare paired outputs, assign severity tags, and provide structured feedback to help retrain models. Experience in evaluation, annotation, and inter-rater calibration is valued.

The role is contractor, remote in the US, with hourly compensation to be confirmed after interview. Ideal candidates can commit to at least 10 hours weekly and bring domain knowledge in linguistics, moderation, or safety review.

Qualifications

• Prior evaluation, annotation, or human-rater experience on harmlessness ranking data evaluation.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and 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 harmlessness ranking preference data evaluation model outputs against a versioned rubric and assign severity tags.
• 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.

Skills

Evaluation experience

Annotation experience

Harmlessness ranking

Inter-rater calibration

Attention to detail

Job description

Harmlessness Ranking Preference Data Reviewer is a remote evaluation track for reviewing harmlessness ranking preference data 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: RLHF & Human Preference Data · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

Harmlessness Ranking Preference Data Reviewer is a remote evaluation track for reviewing harmfulness ranking preference data evaluation prompts and responses against AuraOne's quality rubric.

About the role

Harmlessness Ranking Preference Data Reviewer is a remote evaluation track for reviewing harmlessness ranking preference data 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 harmlessness ranking preference data evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Produce preference rankings, reward-model feedback, and calibrated human judgment for post-training pipelines.

Responsibilities

• Evaluate harmlessness ranking preference data evaluation model outputs against a versioned rubric and assign severity tags for Harmlessness Ranking Preference Data 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.

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 harmlessness ranking preference data evaluation or adjacent content for Harmlessness Ranking Preference Data 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.

Role signals

Example tasks

• Compare two harmlessness ranking preference data 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
• Harmlessness Ranking Preference Data evaluation
• Preference ranking
• RLHF
• Rater calibration
• Harmlessness
• Ranking