AI Safety Expert – English, Tamil

This is a fully remote position, open to applicants in United States.

📋 Description

• Conduct red-team assessments of conversational AI models and agents through methods such as jailbreaks, prompt injections, misuse cases, bias exploitation, and multi-turn manipulation.

• Create human data by annotating failures, classifying vulnerabilities, and identifying systemic risks.

• Utilize taxonomies, benchmarks, and playbooks to ensure consistent testing practices.

• Generate reproducible reports, datasets, and attack scenarios.

• Evaluate AI outputs related to sensitive subjects, including bias, misinformation, and harmful behaviors.

• Enhance evaluation breadth and discover vulnerabilities that automated tests may overlook.

• Support Mercor’s AI-lab and enterprise clients in training and improving cutting-edge AI models.

⛳️ Requirements

• Proficiency in both English and Tamil is required.

• Strong discernment regarding language and content; capability to evaluate the accuracy, completeness, and appropriateness of AI responses.

• Skill in identifying subtle errors, inconsistencies, and omissions.

• Ability to consistently adhere to guidelines, taxonomies, benchmarks, playbooks, and quality standards.

• Capability to articulate reasoning clearly to both technical and non-technical audiences.

• Flexibility to work across various projects, task types, and client demands.

• Must hold independent contractor status.

• Currently unable to accommodate H1-B or STEM OPT candidates.

• Preferred expertise includes adversarial machine learning, cybersecurity, socio-technical risk, and creative probing.

🏝️ Benefits

• Fully remote position.

• Flexible working hours; manage your own schedule.

• Weekly compensation through Stripe or Wise.

• Competitive salary.

• Access to wellness resources and clear protocols for projects with higher sensitivity.

• Project timelines may be extended, shortened, or concluded early based on needs and performance.

• Referral bonuses of up to $90 for each successful candidate referral.