Security Engineer

Research and develop approaches to AI red teaming, adversarial testing, security evaluation, and robust inference Threat model agentic AI architectures, trust boundaries, attack surfaces, privileged capabilities, and failure modes Design adversarial evaluations for prompt injection, tool abuse, privilege escalation, data exfiltration, poisoning, and unintended agent behavior Build automated security evaluation and regression frameworks for agents, models, tools, and infrastructure Translate attacks and research findings into production mitigations, architectural improvements, and reusable security controls Design secure execution environments, sandboxing, isolation mechanisms, capability boundaries, permission models, and least-privilege controls Develop scalable AI infrastructure and services for secure model inference and agent execution Own and improve production infrastructure across Kubernetes, AWS, networking, storage, and compute Implement controls for IAM, secrets, network isolation, containers, and service-to-service communication Build scalable APIs, internal platform services, and infrastructure tooling Improve observability through structured logging, metrics, distributed tracing, dashboards, security telemetry, and automated alerting Investigate production and security failures across models, agents, distributed systems, and infrastructure Optimize performance, latency, and infrastructure cost while maintaining reliability and security Track emerging attacks against LLMs and agentic systems and translate research into evaluations and defenses Collaborate directly with engineers building the agent runtime, evaluation infrastructure, tools, and production AI systems Requirements U.S. Citizenship is required 5+ years of experience building production software, backend infrastructure, distributed systems, security systems, or AI/ML infrastructure Bachelor's, Master's, or Doctorate in Computer Science, Computer Engineering, Cybersecurity, Data Science, or a related technical field, or equivalent practical experience Demonstrated experience in AI red teaming, adversarial machine learning, offensive security, systems security, or related security research Experience evaluating AI systems beyond basic direct prompt injection attacks Strong understanding of modern LLM and agentic systems, including model inference, context management, tool use, retrieval, and multi-step agent execution Experience threat modeling complex systems and translating risks into engineering controls Strong programming experience in Python and production-quality software development Experience operating production services on Kubernetes and cloud platforms such as AWS, GCP, or Azure Strong understanding of networking, distributed systems, containers, service orchestration, and scalable architectures Experience designing APIs, services, asynchronous systems, and event-driven architectures Ability to debug failures spanning application code, AI models, distributed systems, and infrastructure Ability to move between research and engineering, validating attacks or defenses experimentally and turning results into production systems Experience with AI agent runtimes and tool-execution environments Experience with secure code execution sandboxes, container isolation, microVMs, or other untrusted-workload mechanisms Experience with cloud security, infrastructure hardening, IAM, secrets management, network isolation, and zero-trust architectures Experience with offensive security, penetration testing, vulnerability research, or exploit development Experience developing automated adversarial evaluations or integrating security evaluations into CI/CD pipelines Experience with adversarial machine learning, model robustness, or inference-time defenses Familiarity with MITRE ATLAS, OWASP LLM/GenAI guidance, or the NIST AI Risk Management Framework Experience securing RAG systems, vector stores, model gateways, or other modern AI infrastructure components Experience with software supply-chain security and securing model, dependency, and container artifacts Experience working in government, defense, or other high-security environments Core Competencies Demonstrates expertise in AI red teaming, adversarial machine learning, and security evaluation, with a strong focus on building and securing production infrastructure across cloud platforms and distributed systems. Proficient in threat modeling, designing secure execution environments, and implementing robust security controls for AI systems. Highest-signal resume keywords AI Red Teaming Adversarial Machine Learning Kubernetes Cloud Security Python Programming Hard Skills Threat Modeling Adversarial Testing Security Evaluation Automated Security Frameworks API Design Distributed Systems Infrastructure Hardening Vulnerability Research Model Inference Event-Driven Architectures Soft Skills Collaboration Problem-Solving Research and Engineering Transition Industry Keywords Cybersecurity AI Infrastructure High-Security Environments MITRE ATLAS NIST AI Risk Management Framework Tools & Technologies AWS GCP Azure CI/CD Pipelines MicroVMs Container Isolation IAM Secrets Management Zero-Trust Architectures Observability Tools #J-18808-Ljbffr