LLM Red-Teamer

AI Trainer Jobs; United States; Remote; USD 34,000 - 76,000; Full time

Job summary

AuraOne is seeking a remote LLM Red-Teamer to review prompts and responses against our quality rubric. You will compare paired outputs, label edge cases, and provide structured feedback for retraining.

This contractor role requires clear written reasoning, strong attention to detail, and reliable async availability for at least 10 hours per week. Compensation is hourly and determined after interview.

Qualifications

• Prior evaluation, annotation, or human-rater experience in llm red teamer evaluation or related work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause applied.
• Strong attention to detail and ability to flag when a prompt is the problem.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate llm red teamer evaluation model outputs against a versioned rubric and assign severity tags.
• 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 to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples weekly.

Skills

Evaluation experience

Written reasoning

Attention to detail

Async availability

Rubric calibration

Inter-rater calibration

Domain expertise

Tools

Rubric-based annotation

LLM evaluation tooling

Job description

Role details

LLM Red-Teamer is a remote evaluation track for reviewing llm red teamer 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

LLM Red-Teamer is a remote evaluation track for reviewing llm red teamer evaluation prompts and responses against AuraOne's quality rubric.

About the role

LLM Red-Teamer is a remote evaluation track for reviewing llm red teamer 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 llm red teamer 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 llm red teamer evaluation model outputs against a versioned rubric and assign severity tags for LLM Red-Teamer 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 llm red teamer evaluation or adjacent content for LLM Red-Teamer 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 llm red teamer 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
• LLM Red Teamer 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

• 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.