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AI Safety Red Teamer, English and Malay

$17–$25/hr

RemoteContracttechnology
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About this role

Help strengthen conversational AI by probing models with adversarial inputs, identifying vulnerabilities, and producing actionable safety data. This remote, text-based role focuses on reviewing AI outputs, including content related to bias, misinformation, and harmful behavior. Participation in higher-sensitivity projects is optional, with topics communicated in advance and supported by clear guidelines and wellness resources.

Key Responsibilities
  • Red-team conversational AI models and agents through jailbreaks, prompt injection, misuse scenarios, bias exploitation, and multi-turn manipulation.
  • Review and annotate AI failures, classify vulnerabilities, and flag systemic risks.
  • Apply established taxonomies, benchmarks, and playbooks to maintain consistent testing.
  • Create reproducible reports, datasets, and attack cases that teams can use to improve AI systems.
Qualifications
  • Native fluency in both English and Malay is required.
  • Prior experience in AI red teaming, cybersecurity, or socio-technical risk probing.
  • Ability to use structured frameworks or benchmarks and explain risks to both technical and non-technical audiences.
  • Comfort working across varied projects and customer needs.
Preferred Experience
  • Adversarial machine learning, including jailbreak datasets, prompt injection, RLHF or DPO attacks, or model extraction.
  • Cybersecurity, including penetration testing, exploit development, or reverse engineering.
  • Socio-technical risk analysis, including harassment or misinformation probing, abuse analysis, or conversational AI testing.
  • Creative adversarial thinking informed by psychology, acting, or writing.
Work Terms
  • Remote, hourly engagement.
Compensation
  • $17 to $25 per hour.
What Success Looks Like
  • Uncover vulnerabilities that automated testing misses.
  • Deliver reproducible artifacts that improve AI-system safety.
  • Expand evaluation coverage across more scenarios and reduce production surprises.

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