AI/ML Engineer, National Security, Google Public Sector

Google · Reston, VA | Washington, DC

senior Machine Learning Engineer ic · Posted Apr 2, 2026
$153,000 – $222,000
USD per year

The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

The GPS AI Catalyst Team (ACT) is dedicated to AI adoption across the public sector. By tightly aligning our engineering efforts with Go-to-Market and Google Public Sector Services, we incubate and productionalize differentiated AI solutions to solve repeatable mission-critical use cases. We serve as the technical backbone and trusted advisors for customers, ensuring the swift and secure integration of AI solutions, from initial concept through sustained deployment.

As an AI/ML Engineer, you will be a key member of the Google Cloud Consulting Professional Service Organization, guiding customers through their business transformation by leveraging Google's AI technologies. You will design, prototype, and implement modern technical architectures. This role involves acting as a trusted technical advisor, resolving challenges, and sharing best practices through various forms of technical content.

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.

The US base salary range for this full-time position is $153,000-$222,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

  • Be a trusted technical advisor to customers and solve complex machine learning challenges.
  • Create and deliver best practice recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of key business and technical stakeholders.
  • Work with customers, partners, and Google product teams to deliver tailored solutions into production.
  • Coach customers on the practical challenges in ML systems: feature extraction/feature definition, data validation, monitoring, and management of features/models.

Minimum qualifications:

  • Bachelor's degree in Computer Science or equivalent practical experience.
  • 6 years of experience building machine learning solutions and working with technical customers.
  • Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.
  • Experience with frameworks for deep learning (e.g., Tensorflow, Jax, PyTorch, Ray, etc.), AI accelerators (e.g., TPUs, GPUs), model architectures (e.g., encoders, decoders, transformers), and using machine learning APIs.
  • Must possess an active Top Secret/SCI security clearance with current polygraph.
  • Ability to travel up to 30% of the time.

Preferred qualifications:

  • Experience with LLMs and LLM-based solutions such as prompt engineering, fine-tuning, RAG workflows, and agentic systems.
  • Experience in containerizing ML workloads within Linux/Unix environments.
  • Ability to lead the design and implementation of AI-based solutions, web services, and debugging tools.
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