Child Safety Red Team Specialist

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

Job summary

AuraOne is seeking a Child Safety Red Team Specialist to remotely stress test AI systems against adversarial prompts. This independent contractor role involves crafting jailbreaks, documenting failures with rigor, and ensuring patches are reproducible before deployment.

Responsibilities include scoring defenses, triaging new attack vectors, and maintaining a library of red-team rubrics to guide safety improvements. Flexible hours and remote work support ongoing program needs.

Qualifications

• Demonstrated experience red-teaming AI systems, security research, or adversarial ML 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.

Responsibilities

• Design adversarial prompts that probe known 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

Red-teaming

Security research

Adversarial ML

Policy taxonomy

Documentation

Multilingual

Job description

Child Safety 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

Child Safety Red Team Specialist is a remote red-team track for stress-testing AI systems against adversarial prompts.

Role details

About the role

Child Safety Red Team Specialist is a remote red-team track for stress-testing AI systems against adversarial prompts.
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 Child Safety 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.
• 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 Child Safety 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
• Child
• Safety

Application boundary

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

Specialist intake

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.