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

engineeringjobs.net, Inc.; Town of Montana (WI); Hybrid; USD 174,000 - 252,000; Full time

Job summary

Google Public Sector seeks a Senior Security Engineer, AI/ML, to design and secure AI/ML deployments across on‑prem GPUs and cloud environments. You will oversee security configuration of AI deployments, from local clusters to cloud services, with emphasis on LLMs, models, and ML pipelines.

You will advance automated defenses and adversarial testing, ensuring a robust security posture for Google's AI infrastructure and products.

Qualifications

• Bachelor's degree in Computer Science, Data Science, AI, or related field or equivalent practical experience.
• 5 years of AI/ML development or AI infrastructure engineering.
• 5 years of Docker and Kubernetes experience.
• 5 years of Python with PyTorch, TensorFlow, or Transformers.
• Willingness to travel up to 25% as needed.
• Active Top Secret/SCI clearance with current polygraph.

Responsibilities

• Architect and manage LLM deployments on‑prem and cloud environments.
• Audit multi‑agent orchestration and vector databases for data flow and privileges.
• Use Docker and Kubernetes to orchestrate scalable inference and training.
• Protect model weights, secure data ingestion, and harden ML endpoints.
• Mitigate AI-specific threats and map findings to MITRE ATLAS/OWASP for LLMs.
• Bridge on‑prem clusters and cloud AI services to maintain security posture.

Skills

AI/ML development

Docker

Kubernetes

Python

Security clearance

Travel up to 25%

Education

Bachelor's degree in CS or related field

Tools

PyTorch

TensorFlow

Hugging Face Transformers

LLM deployment frameworks

Job description

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

Location: Washington D.C., DC, USA; Maryland, USA

Remote eligible

Experience Level: Mid

Mid experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

Note: Applicants must work 5 days per week on-site in Fort Meade, Maryland.

Benefits

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: 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.
• 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 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.

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 grow our team to meet the complex needs of local, state and federal government and educational institutions.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US Pay Range

$174,000 - $252,000 (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 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.

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Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy, Know your rights: workplace discrimination is illegal, and Belonging at Google, and How we hire.

If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

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