AI Safety & Red Teaming Specialist

Role Title: AI Safety & Red Teaming Specialist
Role Type: Contractor
Location: Remote

We are looking for an experienced AI Safety & Red Teaming Specialist to help evaluate and strengthen the safety, security, and robustness of next-generation AI systems. In this role, you will leverage your expertise in adversarial AI, LLM security, and AI safety to identify vulnerabilities, design evaluation methodologies, and improve the resilience of large language models against real-world threats.

This opportunity is ideal for professionals passionate about AI security, ethical hacking, and developing robust evaluation frameworks for advanced AI systems.

• 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.

• 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.

• → 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.
• → Build, maintain, and enhance regression test suites to continuously assess jailbreak susceptibility and prompt injection risks.
• → 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.
• → Document testing methodologies, findings, and best practices through clear technical reports and presentations for both technical and non-technical stakeholders.

• AI Safety
• LLM Red Teaming
• Prompt Injection
• Adversarial Machine Learning

• Ethical Jailbreaking
• AI Security
• Prompt Engineering
• AI Evaluation Frameworks