Autonomous Replication Risk Red Team Specialist

AI Trainer Jobs; United States; Remote; USD 90,000 - 96,000; Full time

Job summary

AI Trainer Jobs is seeking an Autonomous Replication Risk Red Team Specialist for a remote, US-eligible contractor role. You will design adversarial prompts, document failures, and map each jailbreak to the violated policy clause to help patch gaps.

You will score defenses across single-turn and multi-turn conversations, triage new attack vectors, and provide clear, reproducible reports to the safety team. The position offers hourly pay and remote work with flexible scheduling.

Qualifications

• Demonstrated experience red-teaming AI systems or adversarial ML work.
• Strong written communication; reports become patch tickets.
• Comfort with policy-grey areas and clear documentation.
• Familiarity with prompt-injection, jailbreak, and policy-bypass taxonomies.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Design adversarial prompts probing known weakness classes.
• Document every successful attack with reproduction steps and policy violated.
• Score defenses across single-turn and multi-turn conversations.
• Triage new attack vectors and route to safety team with severity.

Skills

Adversarial prompting

Red-team analysis

Policy taxonomy

Failure documentation

Adversarial testing

AI safety

Red-team evaluation

Policy rubrics

Autonomous replication

Risk assessment

Job description

Autonomous Replication Risk Red Team Specialist 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

Autonomous Replication Risk Red Team Specialist is a remote red-team track for stress-testing AI systems against adversarial prompts.

About the role

Autonomous Replication Risk Red Team Specialist 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 Autonomous Replication Risk Red Team Specialist 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 Autonomous Replication Risk Red Team Specialist 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
• Adversarial prompt testing
• AI safety
• Red-team evaluation
• Policy rubrics
• Autonomous
• Replication
• Risk