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

About the role

Note: Google's hybrid workplace includes remote and in-office roles. By applying to this position you will have an opportunity to share your preferred working location from the following: In-office locations: Washington D.C., DC, USA; Fort Meade, MD, USA. Remote location(s): Maryland, USA.

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 harden inference endpoints across the Machine Learning operations (MLOps) lifecycle.
• Investigate and mitigate AI-specific threats (e.g., prompt injection, jailbreaking, data poisoning).
• 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.

Requirements

• 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 the time as needed.
• Must possess an active Top Secret/SCI security clearance with current polygraph.

Qualifications

• 5 years of experience in AI/ML research or software development.
• Experience with LLM deployment frameworks such as vLLM, NVIDIA Triton, or Ollama and agent development.
• Knowledge of open worldwide application security project (OWASP) for LLMs or similar security frameworks.
• Familiarity with cloud-native AI services (e.g., cloud computing platform, Google Vertex AI).
• Track record of deploying AI models on air-gapped or on-premises high-performance computing (HPC) systems.

Skills

• Understanding of how Large Language Models (LLMs) work under the hood.
• Development of the next generation of automated defenses and adversarial testing frameworks.

Benefits

Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits

Pay

US: $174000 - $253000 (USD)

Schedule

Full-time, 5 days per week on-site in Fort Meade, Maryland