Red Team Safety Classifier Evaluation AI Trainer, $55–$65/hour
Project Overview:
Join a growing community of professionals advancing the next wave of AI. As an AI Trainer, you’ll play a hands-on role by analyzing and providing feedback on data to improve LLM performance, helping ensure that the next generation of AI technology is accurate and trustworthy.
We are seeking a skilled AI Safety Evaluator / Red Team Prompt Engineer to work as a project consultant in our AI Labor Marketplace. This is not a full-time employment position — you will be engaged as an expert project consultant on a contract basis.
Location: U.S.-based experts only
Engagement: Part-time, project-based expert evaluation work
Work Type: Remote
Project Summary:
A fast-paced AI safety evaluation sprint focused on adversarial prompt generation and safety classification. Contributors will create and assess high-difficulty, edge-case scenarios, applying structured labeling, severity scoring, and policy-based reasoning to improve model safety performance.
Consultant Engagement Terms:
This is a project-based consultant role. Consultants will be paid on a per-project basis; hourly rates are estimates based on anticipated completion time. Consultants control their own schedule, provide their own tools, and may simultaneously provide services to other vendors/employers (subject to those vendors’ allowances).
Responsibilities:
Contributors will:
• Design adversarial prompts that expose edge cases in AI safety systems
• Apply structured safety classifications, including category and severity
• Write concise, policy-grounded rationales for decisions
• Review and validate peer submissions for accuracy and quality
• Identify ambiguous or difficult-to-classify scenarios
• Maintain consistency across high-volume evaluation tasks
Expected Outcomes:
• High-quality adversarial examples suitable for model evaluation
• Accurate and consistent safety labels and severity ratings
• Clear, defensible rationales aligned with policy guidelines
• Reliable QA feedback improving dataset quality
Qualifications:
• Experience in AI safety, LLM evaluation, red teaming, or trust & safety
• Strong prompt engineering and analytical reasoning skills
• Familiarity with safety taxonomies and policy-based classification
• Ability to work independently and maintain high-quality output
• Prior experience with annotation or evaluation platforms preferred
Apply tot his job
Apply To this Job
Join a growing community of professionals advancing the next wave of AI. As an AI Trainer, you’ll play a hands-on role by analyzing and providing feedback on data to improve LLM performance, helping ensure that the next generation of AI technology is accurate and trustworthy.
We are seeking a skilled AI Safety Evaluator / Red Team Prompt Engineer to work as a project consultant in our AI Labor Marketplace. This is not a full-time employment position — you will be engaged as an expert project consultant on a contract basis.
Location: U.S.-based experts only
Engagement: Part-time, project-based expert evaluation work
Work Type: Remote
Project Summary:
A fast-paced AI safety evaluation sprint focused on adversarial prompt generation and safety classification. Contributors will create and assess high-difficulty, edge-case scenarios, applying structured labeling, severity scoring, and policy-based reasoning to improve model safety performance.
Consultant Engagement Terms:
This is a project-based consultant role. Consultants will be paid on a per-project basis; hourly rates are estimates based on anticipated completion time. Consultants control their own schedule, provide their own tools, and may simultaneously provide services to other vendors/employers (subject to those vendors’ allowances).
Responsibilities:
Contributors will:
• Design adversarial prompts that expose edge cases in AI safety systems
• Apply structured safety classifications, including category and severity
• Write concise, policy-grounded rationales for decisions
• Review and validate peer submissions for accuracy and quality
• Identify ambiguous or difficult-to-classify scenarios
• Maintain consistency across high-volume evaluation tasks
Expected Outcomes:
• High-quality adversarial examples suitable for model evaluation
• Accurate and consistent safety labels and severity ratings
• Clear, defensible rationales aligned with policy guidelines
• Reliable QA feedback improving dataset quality
Qualifications:
• Experience in AI safety, LLM evaluation, red teaming, or trust & safety
• Strong prompt engineering and analytical reasoning skills
• Familiarity with safety taxonomies and policy-based classification
• Ability to work independently and maintain high-quality output
• Prior experience with annotation or evaluation platforms preferred
Apply tot his job
Apply To this Job