Manipulation Risk Risk Evaluator

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

Job summary

AuraOne is seeking a Manipulation Risk Risk Evaluator for a remote, contractor role focused on stress-testing AI systems against adversarial prompts. Reviewers craft attack scenarios, document failures, and pair each jailbreak with the violated rubric clause so the safety team can patch gaps.

Adversarial evaluation helps harden models before they ship to customers. Applicants should push on refusal boundaries, document rigorously, and score defenses across single-turn and multi-turn

Qualifications

• Experience red-teaming AI systems or adversarial ML work.
• Ability to document attacks with reproduction steps and patch tickets.
• Strong written communication and ability to document policy-rationale clearly.

Responsibilities

• Design adversarial prompts that probe weakness classes (jailbreak, policy bypass, prompt injection).
• 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.

Skills

Adversarial prompting

Red-team analysis

Policy taxonomy

Failure documentation

Manipulation Risk Risk evaluation

AI safety

Red-team evaluation

Policy rubrics

Manipulation

Risk

Job description

Manipulation Risk Risk Evaluator 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

Manipulation Risk Risk Evaluator is a remote red-team track for stress-testing AI systems against adversarial prompts.

About the role

Manipulation Risk Risk Evaluator 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 Manipulation Risk Risk Evaluator 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.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 Manipulation Risk Risk Evaluator 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
• Manipulation Risk Risk evaluation
• AI safety
• Red-team evaluation
• Policy rubrics
• Manipulation
• Risk