Requisition Id 15937
Overview:We are seeking an AI Security Systems Architect to design and develop state-of-the-art systems for security testing and evaluation of artificial intelligence technologies. This role involves creating scalable infrastructure to support cutting-edge adversarial testing methodologies, such as red team vs. blue team exercises and AI-on-AI evaluation frameworks.
The ideal candidate will bring a strong foundation in systems architecture, a working knowledge of cluster computing and scaling, and a passion for advancing the security of AI systems under real-world and simulated conditions. This position is critical for ensuring that AI systems remain resilient, robust, and secure against evolving threats. This person will play a key role within ORNL's Center for AI Security Research (CAISER) where he or she will work to advance the state-of-the-art in Automated, Agentic workflows for AI Security research, testing and evaluation.
Key Responsibilities:- Design and Development for Security Testing
- Architect and implement scalable systems tailored specifically for security testing and evaluation of AI systems.
- Develop frameworks to support red/blue team exercises in simulated environments, enabling manual and automated adversarial testing at scale.
- Build and integrate AI-on-AI testing infrastructures, where AI models can actively challenge each other in adversarial contexts to detect vulnerabilities or weaknesses.
- Scalability and Cluster Computing
- Design distributed systems that support high-throughput simulations and stress-testing of AI systems under adversarial conditions.
- Implement cluster computing solutions to efficiently scale testing environments supporting large datasets and high-performance AI workloads.
- Optimize resource allocation for simultaneous testing tasks and real-time tracking of security metrics.
- Adversarial and Threat Modeling Infrastructure
- Develop systems to automate the generation and execution of diverse adversarial testing scenarios, including techniques for perturbation, poisoning, and evasion attacks.
- Design platforms for threat modeling in AI systems, enabling comprehensive vulnerability assessments tailored to diverse use cases, from cloud-hosted models to edge deployments.
- Enable rapid prototyping and iteration for adversarial defenses integrated into the architectural design.
- Collaboration and Security Validation
- Work closely with security specialists, AI researchers, and DevSecOps teams to evaluate and validate the security of AI systems aligned with organizational security standards.
- Partner with stakeholders to design customized testing environments that simulate real-world attack and defense scenarios in production-like conditions.
- Leadership and Innovation
- Lead cross-functional initiatives focused on advancing the security testing capabilities for next-generation AI systems.
- Stay informed of emerging adversarial AI threats, testing methodologies, and scaling innovations to foster continuous improvement in security testing architectures.
- Mentor junior engineers and provide technical leadership in AI security evaluation mechanisms.
Required Qualifications- Master's Degree in Computer Science, Computer Engineering, Cybersecurity, or related fields with 7-10 years of experience or PhD in Computer Science, Computer Engineering, Cybersecurity, or related fields with 2-4 years of experience.
- Proven experience architecting and implementing complex distributed systems tailored for security testing or evaluation at scale.
- Demonstrated expertise in cluster computing and scaling for high-performance environments, with hands-on experience in frameworks such as Hadoop, Spark, or Kubernetes.
Preferred Qualifications- Familiarity with techniques for AI-on-AI adversarial evaluation, including reinforcement learning-based adversarial testing setups.
- Expertise in designing systems that support red/blue team operations alongside DevSecOps integrations.
- Knowledge of privacy-preserving AI methods, secure federated learning, and cryptographic protections.
- Research or publication experience in adversarial testing, distributed systems, and AI system security.
- Experience in supporting continuous integration pipelines for AI security validation in production environments.
Special Requirements: - Q clearance with SCI:This position requires the ability to obtain and maintain a Secret Compartmented Information (SCI) clearance from the Department of Energy. As such, this position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a pre-placement drug test and participation in an ongoing random drug testing program. In addition, due the SCI, you may also be subject to random polygraph testing.
