Lead applied research on adversarial AI agents and coordinated multi-agent or swarm-like behavior. Characterize coordination patterns, behavioral signatures, provenance, and adaptation across agent populations. Build instrumented environments for observing and measuring agent coordination under controlled security scenarios. Develop and evaluate methods to detect, attribute, interrupt, and contain coordinated offensive agent swarms. Stress-test methods across changing models, tools, topologies, and operating conditions to identify evasion paths and failure modes. Produce reproducible evaluation assets and security guidance for red-team and defense partners. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Prior research or engineering experience in agentic AI security, including coordinated, multi-agent, or swarm-like behavior. Demonstrated record of independently leading complex research projects from problem formulation through reproducible results and partner adoption. Peer-reviewed publications, patents, or released research software in agentic AI security, adversarial machine learning, or threat detection. Prior experience designing instrumented security experiments or simulations for observing and evaluating coordinated agent behavior. Prior experience with behavioral analysis, provenance, detection, or attribution methods for AI systems or agents. Demonstrated research or engineering track record in AI security, adversarial machine learning, or cybersecurity. Demonstrated hands-on proficiency with Python and machine learning frameworks for research prototyping and experimentation.