We are seeking experienced AI Safety Red Teamers to identify vulnerabilities in frontier AI systems through adversarial testing. You will design challenging prompts, uncover model weaknesses, and evaluate AI behavior across complex, high-risk, and ambiguous ("grey-area") topics.
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.
Required Qualifications
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.
Preferred Qualifications
Experience with AI Red Teaming, RLHF, SFT, AI Alignment, or Trust & Safety.
Familiarity with jailbreak testing, prompt engineering, or adversarial evaluation methodologies.
Expertise in one or more grey-area domains, including cyber, biosecurity, political content, misinformation, or scientific safety.
Why Join?
Help secure and strengthen the next generation of frontier AI models.
Work on cutting-edge adversarial testing alongside leading AI researchers and safety teams.
Influence how AI systems respond to complex, real-world safety challenges.
Listing sourced from Mercor. Annotation Academy is independent of these platforms and does not guarantee work or pay. See our disclosures.