AI Security Systems Architect

Requisition

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 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…