Senior Security Engineer, AI/ML, National Security, Public Sector

About the job Our Security team works to create and maintain the safest operating environment for Google's users and developers. Security Engineers work with network equipment and actively monitor our systems for attacks and intrusions. In this role, you will also work with software engineers to proactively identify and fix security flaws and vulnerabilities. In this role, you will help us build the most resilient AI infrastructure in the world. This role is designed for a technical expert in Artificial Intelligence and Machine Learning, with a primary interest in how those systems can be defended against adversarial manipulation. You will be responsible for the security configuration of AI deployments, from local on-prem GPU clusters to cloud-native environments. You will understand the nuances of LLMs, neural networks, and containerized ML pipelines, and will apply that knowledge to the frontier of security. You will have an understanding of how Large Language Models (LLMs) work under the hood and to develop the next generation of automated defenses and adversarial testing frameworks. Google Public Sector brings the magic of Google to the mission of government and education with solutions purpose-built for enterprises. We focus on helping United States public sector institutions accelerate their digital transformations, and we continue to make significant investments and [... source excerpt omitted ...] ding job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Architect and manage LLM deployments across on-premises (NVIDIA/AMD) and cloud (cloud computing platform, Google Cloud platform (GCP) environments. Audit multi-agent orchestration, agent construction, and vector databases to map data flows and enforce privilege boundaries. Use Docker and Kubernetes to orchestrate scalable inference and training environments, optimizing Graphics Processing Unit (GPU) utilization and resource isolation. Protect model weights, secure data ingestion, and hard [... source excerpt omitted ...] Map testing findings to MITRE ATLAS, OWASP for LLMs, and STRIDE models. Bridge local high-compute clusters and cloud AI services while maintaining a consistent security posture. Qualifications Minimum qualifications: Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field or equivalent practical experience. 5 years of experience in AI/ML development, AI infrastructure engineering, or software development. 5 years of experience with containerization (Docker) and orchestration (Kubernetes). 5 years of experience with Python and with libraries like PyTorch, TensorFlow, or Hugging Face Transformers. Ability to travel up to 25% of t