AI Security Systems Architect

AI Security Systems Architect

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 Dev Sec Ops  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 Dev Sec Ops  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.

About ORNL:

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation's most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an…