Biology Expert (PhD) AI Safety

Biology Expert (PhD) — AI Safety 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 Biology Expert (PhD) — AI Safety is a remote red-team track for stress-testing AI systems against adversarial prompts. About the role Biology Expert (PhD) — AI Safety 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 Biology Expert (PhD) — AI Safety 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. What you should bring Demonstrated experience red-teaming AI systems, security research, or adversarial ML work for Biology Expert (PhD) — AI Safety 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. 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 $65–$70 / hr 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 Biology AI Safety 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. #J-18808-Ljbffr