Trust and Safety Policy AI Evaluator

Trust and Safety Policy AI Evaluator is a remote red-team track for stress-testing AI systems against adversarial prompts. Reviewers craft attack scenarios, document the failure mode, and pair each successful jailbreak with the rubric clause it violated so the safety team can patch the gap. Category: AI Safety & Red Teaming · Pay: $65–$70 / hr · Location: Remote — US-eligible · Contractor Trust and Safety Policy AI Evaluator is a remote red-team track for stress-testing AI systems against adversarial prompts. About the role Trust and Safety Policy AI Evaluator is a remote red-team track for stress-testing AI systems against adversarial prompts. Reviewers craft attack scenarios, document the failure mode, and pair each successful jailbreak with the rubric clause it violated so the safety team can patch the gap. Adversarial evaluation is how AuraOne hardens AI models before they ship to customers. Reviewers think like attackers and write up failures with enough rigor that the modeling team can reproduce, fix, and regress-test them. Push on refusal boundaries and dual-use risk before a model ships. Responsibilities Design adversarial prompts that probe known weakness classes (jailbreak, policy bypass, prompt injection) for Trust and Safety Policy AI Evaluator assignments. Document every successful attack with reproduction steps and the policy clause it violated. Score model defenses across single-turn and multi-turn conversations. Triage emerging attack vectors and route them to the safety team with severity ratings. Maintain a personal library of attack patterns and propose new red-team rubrics. Role details Track Adversarial evaluation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US What you should bring Demonstrated experience red-teaming AI systems, security research, or adversarial ML work for Trust and Safety Policy AI Evaluator work. Strong written communication — your reports become the patch ticket. Comfort working in policy-grey areas with clear documentation of what was attempted and why. Familiarity with prompt-injection, jailbreak, and policy-bypass taxonomies. Reliable async availability for at least 10 hours per week. Role signals Example tasks Construct a 5-turn adversarial conversation that bypasses a specific policy clause and write up the patch ticket. Score a model's defenses against a known jailbreak pattern across 20 variants. Propose a new red-team rubric category after spotting an emerging attack vector. Reproduce a failure another reviewer reported and confirm the severity tag. Useful experience Background in offensive security, AppSec, or trust & safety operations. Experience publishing or reproducing public adversarial-ML research. Multilingual fluency for cross-language attack testing. 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 Adversarial prompting Red-team analysis Policy taxonomy Failure documentation Trust and Safety Policy AI evaluation Legal reasoning Policy review Risk analysis Trust Safety Policy #J-18808-Ljbffr AI Trainer Jobs