Senior AI Trainer

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

Job summary

AuraOne is seeking a Senior AI Trainer for a remote evaluation track. Review prompts and model outputs against a quality rubric, compare paired responses, and provide structured feedback to retrain the model.

The role emphasizes labeling edge cases, documenting rationale, and maintaining calibration with gold standards. The position is contractor-based with tasks defined by program volume, available on a remote basis for US-eligible candidates.

Qualifications

• Prior evaluation, annotation, or human-rater experience on Senior AI Trainer work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning naming the issue and rubric clause being applied.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate senior AI trainer evaluation model outputs against a versioned rubric and assign severity tags for Senior AI Trainer 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.

Skills

Evaluation experience

Annotation experience

Inter-rater calibration

Attention to detail

Job description

Role details

Senior AI Trainer is a remote evaluation track for reviewing senior ai trainer 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

About the role

Senior AI Trainer is a remote evaluation track for reviewing senior ai trainer 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 senior ai trainer 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 senior ai trainer evaluation model outputs against a versioned rubric and assign severity tags for Senior AI Trainer 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 senior ai trainer evaluation or adjacent content for Senior AI Trainer 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 senior ai trainer 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
• Senior AI Trainer evaluation
• AI model 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.