AI Safety Experts — English & Urdu

AuraOne; United States; On-site; USD 22,000 - 30,000; Full time

Job summary

AuraOne is seeking AI Safety Experts for a remote, contractor-style role focused on stress-testing AI systems with adversarial prompts. You will design and document jailbreak scenarios, score defenses, and contribute to evolving red-team rubrics from a cross-language testing perspective.

Work is remote with US-eligibility, demanding rigorous documentation of failures and precise patch tickets for the safety team.

Qualifications

• Demonstrated experience red-teaming AI systems, security research, or adversarial ML work.
• Strong written communication — reports become patch tickets.
• Comfort working in policy-grey areas with clear documentation of what was attempted and why.
• Familiarity with prompt-injection, jailbreak, and policy-bypass taxonomies.
• Fluency in Urdu and cross-language attack testing.

Responsibilities

• Design adversarial prompts that probe known weakness classes (jailbreak, policy bypass, prompt injection) for AI Safety Experts — English & Urdu 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.

Skills

Adversarial prompting

Red-team analysis

Policy taxonomy

Failure documentation

Urdu generalist evaluation

Multilingual fluency

Job description

AI Safety Experts — English & Urdu is a remote red-team track for stress-testing AI systems against adversarial prompts.

Category: AI Safety & Red Teaming · Pay: $16–$22 / hr · Location: Remote — US-eligible · Contractor

AI Safety Experts — English & Urdu 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.

About the role

AI Safety Experts — English & Urdu 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 AI Safety Experts — English & Urdu 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.

What you should bring

• Demonstrated experience red-teaming AI systems, security research, or adversarial ML work for AI Safety Experts — English & Urdu 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

$16–$22 / hr
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
• Urdu generalist evaluation