Air - Senior AI Engineer, Security Infrastructure
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 how modern LLM and agentic systems operate, including model inference, context management, tool use, retrieval, and multi-step agent execution. • Strong intuition for how AI systems can fail when exposed to adversarial users, untrusted data, external tools, and complex production environments. • Experience threat modeling complex systems and translating identified risks into concrete engineering controls. • Strong programming experience in Python and experience building production-quality software. • 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. • Comfortable debugging failures that span application code, AI models, distributed systems, and infrastructure. • Able to move between research and engineering: reading new research, developing an attack or defense, validating it experimentally, and turning the result into a production system. • Passionate about building secure AI systems and proactively defending against novel attack vectors. • Experience designing or securing AI agent runtimes and tool-execution environments. • Experience building secure code execution sandboxes, container isolation, microVMs, or other mechanisms for running untrusted workloads. • 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 AI security frameworks and threat taxonomies such as MITRE ATLAS, OWASP guidance for LLM/GenAI applications, or the NIST AI Risk Management Framework. • Experience securing RAG systems, vector stores, model gateways, or other components of modern AI infrastructure. • Experience with software supply-chain security and securing model, dependency, and container artifacts. • Experience working in government, defense, or other high-security environments. • We firmly believe that past performance is the best indicator of future performance. If you thrive while building solutions to complex problems, are a self-starter, and are passionate about making an impact in global security, we’re eager to hear from you.
Responsibilities
• Research and develop new approaches to AI red teaming, adversarial testing, security evaluation, and robust inference. • Threat model agentic AI architectures, identifying trust boundaries, attack surfaces, privileged capabilities, and potential failure modes. • Design adversarial evaluations targeting threats such as prompt injection, indirect prompt injection, tool abuse, privilege escalation, data exfiltration, context or memory poisoning, and unintended agent behavior. • Build automated security evaluation and regression frameworks that continuously test agents, models, tools, and infrastructure against known and emerging attacks. • Translate successful attacks and research findings into production mitigations, architectural improvements, and reusable security controls. • Design secure execution environments for AI agents interacting with tools, code, data, and external systems. • Build and harden sandboxing and isolation mechanisms for executing agent-generated or otherwise untrusted workloads. • Design capability boundaries, permission models, and least-privilege access controls for agent tools and services. • Develop scalable AI infrastructure and services supporting secure model inference and agent execution. • Own and improve production infrastructure across Kubernetes, AWS, networking, storage, and compute. • Implement security controls around identity and access management, secrets, network isolation, containers, and service-to-service communication. • Build scalable APIs, internal platform services, and infrastructure tooling that improve developer productivity, system reliability, and security. • Improve observability across AI systems through structured logging, metrics, distributed tracing, dashboards, security telemetry, and automated alerting. • Investigate complex production and security failures across models, agents, distributed systems, and infrastructure. • Optimize performance, latency, and infrastructure cost while maintaining strong reliability and security guarantees. • Stay current with emerging attacks against LLMs and agentic systems and rapidly translate relevant research into practical evaluations and defenses.
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