Senior AI Engineer, Security Infrastructure

• 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

• 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

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.

• AI Red Teaming
• Adversarial Machine Learning
• Kubernetes
• Cloud Security
• Python Programming

• Threat Modeling
• Adversarial Testing
• Security Evaluation
• Automated Security Frameworks
• API Design
• Distributed Systems
• Infrastructure Hardening
• Vulnerability Research
• Model Inference
• Event-Driven Architectures

• Collaboration
• Problem-Solving
• Research and Engineering Transition

• Cybersecurity
• AI Infrastructure
• High-Security Environments
• MITRE ATLAS
• NIST AI Risk…