LLM Red Team Specialist — Failure Modes & Edge Cases

LLM Red Team Specialist — Failure Modes & Edge Cases 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: $60–$90 / hr
·

Location:

Remote — US-eligible
· Contractor

LLM Red Team Specialist — Failure Modes & Edge Cases is a remote red-team track for stress-testing AI systems against adversarial prompts.

LLM Red Team Specialist — Failure Modes & Edge Cases 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 Aura One 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.

• Design adversarial prompts that probe known weakness classes (jailbreak, policy bypass, prompt injection) for LLM Red Team Specialist — Failure Modes & Edge Cases 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.

Track Adversarial evaluation Work model Remote
· Independent specialist contractor Compensation $60–$90 / hr Eligible from US

• Demonstrated experience red-teaming AI systems, security research, or adversarial ML work for LLM Red Team Specialist — Failure Modes & Edge Cases 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.

• 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.

• Background in offensive security, App Sec, or trust & safety operations.
• Experience publishing or reproducing public adversarial-ML research.
• Multilingual fluency for cross-language attack testing.

$60–$90 / hr
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

• Adversarial prompting
• Red-team analysis
• Policy taxonomy
• Failure documentation
• Adversarial prompt testing

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.
- 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.