AI Safety Expert, English, Punjabi
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Job Description
AI red-teaming experts probing conversational models for Mercor’s frontier AI safety projects. Generating vulnerability data, attack cases, and reproducible reports for AI labs and enterprises.
Responsibilities:
- Red-team conversational AI models and agents using jailbreaks, prompt injections, misuse cases, bias exploitation, and multi-turn manipulation
- Annotate failures, classify vulnerabilities, and flag systemic risks
- Apply taxonomies, benchmarks, and playbooks to keep testing consistent
- Produce reproducible reports, datasets, and attack cases
- Review AI outputs involving sensitive topics such as bias, misinformation, or harmful behaviors
- Uncover vulnerabilities automated tests miss
- Expand evaluation coverage and reduce production surprises
- Strengthen customer AI systems through human data-driven red teaming
Requirements:
- Native fluency in English and Punjabi is required
- Strong judgment about language and content, including assessing whether AI responses are accurate, complete, and appropriate
- Ability to explain reasoning clearly to technical and non-technical audiences
- Rigorous attention to subtle errors, inconsistencies, and gaps
- Ability to follow guidelines and quality standards consistently
- Adaptability across projects, task types, and customers
- Independent contractor status
- Must not be an H1-B or STEM OPT candidate
- Nice-to-have specialties: adversarial ML, cybersecurity, socio-technical risk, or creative probing
Benefits:
- Fully remote role that can be completed on your own schedule
- Payments weekly on Stripe or Wise based on services rendered
- Higher-sensitivity projects are optional
- Clear guidelines and wellness resources for sensitive-content work
- Reasonable accommodations upon request
- Opportunity to build experience in human data-driven AI red teaming
- Direct role in making AI systems more robust, safe, and trustworthy
- Collaboration with leading researchers
- Referral payments of up to $90 for each successful referral, subject to limits
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