Biology Expert (PhD) — AI Safety is a remote red-team track for stress-testing AI systems against adversarial prompts.
ContractorRemote — US-eligible$65–$70 / hr
Static snapshot. Listings are generated from bundled local jobs data last refreshed on July 1, 2026. Confirm availability through AuraOne intake before relying on a 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.
Stress-test model behavior, refusal boundaries, dual-use risk, and safety policy adherence before deployment.
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.
Calibrate against the broader red-team cohort to keep coverage and severity consistent.
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.
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
$65–$70 / hr
Expected arrangement: contractor, with program-defined task volume and review pacing. A snapshot does not guarantee current placement availability.
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 snapshot and its source to your candidate record.
