Postdoctoral Associate – AI Security

Job Description Summary

Organization’s Summary Statement:
The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland is a University-Affiliated Research Center (UARC) dedicated to advancing research, innovation, and technology transition to improve decision making for U.S. national security. ARLIS combines deep scientific expertise with operational insight to address challenges in intelligence analysis, cybersecurity, artificial intelligence / machine learning, quantum science, and human-machine teaming.

Researchers, scientists, engineers, and analysts at ARLIS collaborate with government agencies, industry partners, and academic institutions to deliver actionable insights and transformative solutions through research and development. Employees at ARLIS work on projects of critical importance, contribute directly to the nation’s security, and are supported by a culture that values integrity, collaboration, and professional growth.

The Applied Research Laboratory for Intelligence and Security (ARLIS) at the University of Maryland is seeking a Postdoctoral Associate in AI Security to conduct cutting-edge research at the intersection of machine learning, cybersecurity, and national security.

This position focuses on advancing the science and practice of securing advanced AI systems against sophisticated adversaries, such as large language models (LLMs), reasoning systems, and agentic architectures. The role operates within a mission-driven R&D environment supporting government and Intelligence Community (IC) partners, where the threat model assumes highly capable actors with deep technical access to deployed systems. Opportunities include basic and open research, publishing in top-tier venues, as well as transitioning capabilities into operational use.

The successful candidate will contribute to frontier research spanning adversarial machine learning, secure AI deployment, and other approaches to security and safety, such as mechanistic interpretability.

• Conduct original research in AI security, including adversarial machine learning, model robustness, and secure AI system design.
• Develop and evaluate novel attack and defense techniques for modern AI systems, including:
  • Mechanistic and white-box analysis of model behavior and safety mechanisms
  • Multi-turn and adaptive adversarial interactions with AI systems
  • Security of reasoning models and agent-based architectures
• Design and implement experimental frameworks for evaluating AI system vulnerabilities across deployment scenarios (e.g., open-weight, API-based, and hybrid systems).
• Apply interpretability techniques (e.g., circuit analysis, feature attribution, sparse autoencoders) to understand internal model behavior and failure modes.
• Contribute to the development of benchmarks, evaluation methodologies, and datasets for AI security research.
• Collaborate with interdisciplinary teams including machine learning researchers, systems engineers, and national security domain experts.
• Translate research findings into actionable insights for government sponsors, including technical reports and briefings.
• Publish research in leading conferences and journals (e.g., NeurIPS, ICML, ICLR, IEEE S&P, CCS), consistent with program objectives.

Must be able to obtain a U.S. security clearance. If selected, you must meet the requirements for access to classified information and will be subject to a government security clearance investigation that includes criminal and credit history checks, as well as verification of U.S. citizenship, birth, education, employment, and military history.

Final offer is contingent upon the candidate’s ability to successfully obtain the necessary interim Secret security clearance, as determined by the U.S. Government, prior to commencing employment.

• Adversarial AI & Red Teaming
  • Adaptive, multi-turn attacks and reasoning-based adversarial strategies
  • Evaluation of model robustness under realistic threat models
• Secure AI Systems & Deployment
  • Security of agentic systems, tool use, and multi-model architectures
  • Supply chain and fine-tuning risks in open-weight models
• AI…