Threat Intelligence Abuse Scenario Red Teamer

AI Trainer Jobs; United States; Remote; USD 96,000 - 138,000; Full time

Job summary

AuraOne is seeking a Threat Intelligence Abuse Scenario Red Teamer to stress-test AI systems with adversarial prompts in a remote contractor role. Reviewers craft attack scenarios, document failure modes, and tie each jailbreak to the violated rubric clause for patching.

You'll evaluate defenses across single-turn and multi-turn interactions, score robustness, and triage emerging vectors for the safety team to address, with hourly compensation after interview.

Qualifications

• Demonstrated experience red-teaming AI systems, security research, or adversarial ML work.
• Familiarity with prompt-injection, jailbreak, and policy-bypass taxonomies.
• Excellent written documentation of attacks and patches.

Responsibilities

• Design adversarial prompts that probe known weakness classes (jailbreak, policy bypass, prompt injection).
• Document every successful attack with reproduction steps and policy clause 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.

Skills

Adversarial prompting

Red-team analysis

Policy taxonomy

Failure documentation

Threat intelligence

Security review

Adversarial testing

Threat modeling

Intelligence

Job description

Threat Intelligence Abuse Scenario Red Teamer 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: Cybersecurity & Adversarial Testing · Pay: $70–$100 / hr · Location: Remote — US-eligible · Contractor

Threat Intelligence Abuse Scenario Red Teamer is a remote red-team track for stress-testing AI systems against adversarial prompts.

About the role

Threat Intelligence Abuse Scenario Red Teamer 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.
Probe AI systems for prompt injection and insecure code. Find the misuse paths.

Responsibilities

• Design adversarial prompts that probe known weakness classes (jailbreak, policy bypass, prompt injection) for Threat Intelligence Abuse Scenario Red Teamer 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.

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 Threat Intelligence Abuse Scenario Red Teamer 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

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
• Threat Intelligence Abuse Scenario Red Teamer evaluation
• Security review
• Adversarial testing
• Threat modeling
• Threat
• Intelligence