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

Senior Security Engineer, AI/ML, National Security, Public Sector corporate_fare Google place Washington

D.C., DC, USA

; Maryland, USA laptop_windows Remote eligible bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area. info_outline X Candidate must work 5 days per week on-site in Fort Meade, Maryland In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees.

Benefits for this role include:

Health, dental, vision, life, disability insurance

Retirement Benefits:

401(k) with company match

Paid Time Off:

20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment

Sick Time:

40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks

Baby Bonding Leave:

18 weeks

Holidays:

13 paid days per year

Note:

Google's hybrid workplace includes remote and in-office roles. By applying to this position you will have an opportunity to your preferred working location from the following: In-office locations: Washington

D.C., DC, USA.

Remote location(s): Maryland, USA.

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

Preferred qualifications:

5 years of experience in AI/ML research or software development. Experience with LLM deployment frameworks such as v

LLM, 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. 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. 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.