Remote RLHF QA Evaluator - Preference Data

AI Trainer Jobs; United States; Remote; USD 28,000 - 55,000; Part time

Benefits offered by this job

Remote work

Job summary

AuraOne is seeking a remote Preference Dataset QA Reward Model Evaluator to review prompts and responses against our quality rubric. You will compare paired outputs, label edge cases, and write structured feedback for the modeling team to retrain.

As a contractor, you work independently on a remote track eligible for US-based candidates, with hourly compensation assessed after the interview. You will produce rankings, tag issues, and help calibrate human judgment for post-training pipelines.

Qualifications

• Prior evaluation, annotation, or human-rater experience in preference dataset QA/reward model evaluation.
• Ability to apply 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.
• Experience with inter-rater agreement metrics and calibration cycles.

Responsibilities

• Evaluate outputs against a versioned rubric and assign severity tags.
• Compare paired responses and justify 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.
• Audit a batch for rubric consistency and report drift to the program lead.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

RLHF

Rater calibration

Preference dataset

Preference ranking

Job description

AuraOne is seeking a remote Preference Dataset QA Reward Model Evaluator to review prompts and responses against our quality rubric. You will compare paired outputs, label edge cases, and write structured feedback for the modeling team to retrain.

As a contractor, you work independently on a remote track eligible for US-based candidates, with hourly compensation assessed after the interview. You will produce rankings, tag issues, and help calibrate human judgment for post-training pipelines.