Bilingual Vietnamese STEM Expert (PhD) — AI Safety

AI Trainer Jobs; United States; Remote; USD 26,000 - 32,000; Full time

Benefits offered by this job

Remote work

Contractor

Flexible schedule

Job summary

AuraOne is seeking a Bilingual Vietnamese STEM Expert (PhD) for an AI Safety remote red-team role. You will design adversarial prompts to test AI systems, document failures with rigorous reproduction steps, and pair each jailbreak with the clause it violated so the safety team can patch gaps.

As a remote contractor, you will review prompts, score defenses, and triage emerging attack vectors, ensuring rigorous patch work before models reach customers.

Qualifications

• Experience red-teaming AI systems and security research.
• Ability to document failures with rigorous reproduction steps.
• Strong written communication for patch tickets.
• Familiarity with prompt-injection, jailbreaks, and policy bypass taxonomies.
• Availability for at least 10 hours per week.

Responsibilities

• Design adversarial prompts probing jailbreak and policy bypass weaknesses.
• Document each successful attack with steps and violated policy.
• Score defenses for single-turn and multi-turn conversations.
• Triage new attack vectors and escalate to safety team.

Skills

Red-team experience

Adversarial ML

Threat modeling

Policy taxonomy

Multilingual testing

Strong written communication

Education

PhD in STEM field

Job description

Bilingual Vietnamese STEM Expert (PhD) — AI Safety 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: $19–$23 / hr · Location: Remote — US-eligible · Contractor

Bilingual Vietnamese STEM Expert (PhD) — AI Safety is a remote red-team track for stress-testing AI systems against adversarial prompts.

About the role

Bilingual Vietnamese STEM Expert (PhD) — AI Safety 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 Bilingual Vietnamese STEM Expert (PhD) — AI Safety 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 $19–$23 / hr Eligible from US

What you should bring

• Demonstrated experience red-teaming AI systems, security research, or adversarial ML work for Bilingual Vietnamese STEM Expert (PhD) — AI Safety 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

$19–$23 / 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
• Vietnamese generalist evaluation