Design, prototype, and evaluate AI and machine learning models for endpoint management and cybersecurity, analyzing large-scale endpoint data and translating research findings into scalable product features.
AI Safety & Red Teaming Specialist
📜 Description
- Design evaluations for jailbreaks, prompt injection, LLM red teaming, and tool-use abuse.
- Develop cross-domain adversarial tests to uncover multi-turn attack patterns and model vulnerabilities.
- Build and maintain regression suites for jailbreak susceptibility and prompt injection risks.
- Create evaluation frameworks that simulate real-world threats and assess AI robustness.
- Collaborate with technical teams to turn security findings into safety improvements.
- Document methodologies, findings, and best practices in reports and presentations.
🛠️ Requirements
- 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.
Full job description
This role is for one of our clients
$50 - $90/hourpay
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.
Requirements
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.
- 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.
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.
Must-Have Skills
- AI Safety
- LLM Red Teaming
- Prompt Injection
- Adversarial Machine Learning
Good-to-Have Skills
- Ethical Jailbreaking
- AI Security
- Prompt Engineering
- AI Evaluation Frameworks
Similar jobs
Search more AI Research Scientist jobsSafetyTech Client #1 | Adversarial Task Writer for AI Security RL Gyms
Design YAML-based adversarial prompt injection scenarios, test them against frontier models, and validate successful tasks that assess whether AI agents can be manipulated into violating safety policies.
Insurance Risk Subject Matter Expert | AI Evaluation Specialist (On-Call)
Evaluate AI-generated finance, audit, insurance, compliance, and legal content by reviewing its accuracy and reasoning, identifying gaps, and providing structured feedback to improve model performance.
Contract Compliance Subject Matter Expert | AI Evaluation Specialist (On-Call)
Evaluate AI-generated finance, audit, insurance, compliance, and legal content by reviewing its accuracy and reasoning, identifying gaps, and providing structured feedback to improve model performance.
Develop and evaluate AI/ML models for educational products, analyze structured and unstructured data, and explore ways to improve personalized learning, content recommendations, and student outcomes.
