AI Safety & Red Teaming Specialist

Key Responsibilities

• Design and implement advanced evaluation methodologies for AI system safety, including ethical jailbreak testing, prompt injection detection, LLM red teaming, and tool-use abuse scenarios.
• Develop cross-domain adversarial testing strategies to uncover complex, multi-turn attack patterns and model vulnerabilities.
• Create comprehensive evaluation frameworks that simulate real-world adversarial threats to improve AI robustness and reliability.
• Collaborate with technical teams to translate security findings into actionable recommendations for AI safety improvements.

Required Qualifications

• 2+ years of experience in AI Safety, Adversarial Machine Learning, LLM Red Teaming, AI Security, or a related field.
• Hands-on experience researching, testing, or identifying vulnerabilities involving prompt injection, ethical jailbreaks, adversarial attacks, or tool-use exploitation.
• Strong understanding of modern LLM architectures, prompt engineering, and AI safety evaluation methodologies.
• Experience developing structured security assessments, regression testing frameworks, and adversarial evaluation strategies.
• Excellent analytical, documentation, and communication skills with the ability to explain complex technical findings clearly.
• Ability to collaborate effectively within cross-functional technical teams.

Preferred Qualifications

• Master's or PhD in Computer Science, Cybersecurity, Machine Learning, Artificial Intelligence, or a related discipline.
• Contributions to AI security research, open-source AI safety tools, conference presentations, or published research.
• Experience with AI model evaluation frameworks, prompt engineering techniques, and AI security assessment tools.
• Background in multidisciplinary AI safety, cybersecurity, or adversarial machine learning projects.