AI Safety Red Teamer

Posted 2hrs ago

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Job Description

AI Safety Red Teamer identifying vulnerabilities in frontier AI models for Mercor. Designing adversarial prompts and evaluating high-risk AI behaviors.

Responsibilities:

  • Design adversarial prompts to stress-test frontier AI models
  • Identify jailbreaks, unsafe behaviours, hallucinations, and policy failures
  • Evaluate model robustness across misinformation, cyber, biosecurity, fraud, political content, and other sensitive domains
  • Document vulnerabilities and contribute to safety benchmarking and red-teaming reports
  • Collaborate with AI researchers to improve model alignment, robustness, and safety
  • Work on projects focused on training and enhancing AI systems

Requirements:

  • Bachelor's degree or higher in Computer Science, Cybersecurity, Journalism, Communications, Psychology, Biology, Chemistry, Public Policy, or a related discipline
  • 5+ years of professional experience in AI Safety, AI Red Teaming, Trust & Safety, cybersecurity, investigative journalism, life sciences, or a related field
  • Strong analytical reasoning, prompt design, and written communication skills
  • Experience designing adversarial prompts or evaluating frontier AI systems
  • Experience with AI Red Teaming, RLHF, SFT, AI Alignment, or Trust & Safety preferred
  • Familiarity with jailbreak testing, prompt engineering, or adversarial evaluation methodologies preferred
  • Expertise in grey-area domains such as cyber, biosecurity, political content, misinformation, or scientific safety preferred
  • Must not require H1-B or STEM OPT sponsorship

Benefits:

  • Fully remote role
  • Work on your own schedule
  • Weekly payments via Stripe or Wise
  • Projects can be extended, shortened, or concluded early depending on needs and performance
  • Competitive pay
  • Collaboration with leading AI researchers and safety teams
  • Opportunity to help shape the next generation of AI systems
  • Up to $340 for each successful referral
  • Reasonable accommodations available upon request