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X Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Reston, VA, USA; Washington D.C., DC, USA.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience programming in C , Java, Python, Kotlin or Go.
• Experience in technical leadership, including defining technical road maps, delivering projects, and maintaining code quality standards.
• Experience in parallel computing paradigms, hardware-level optimization, and low-level accelerator optimization.

Preferred qualifications:

• Master's degree or PhD in a quantitative discipline (e.g., Computer Science, Physics, Applied Mathematics, or similar).
• 8 years of experience designing, building, and operating large-scale distributed data systems and production machine learning deployments.
• Experience deploying modern deep learning architectures using frameworks like PyTorch or TensorFlow on large-scale clusters.
• Experience with cloud-native infrastructure (Docker, Kubernetes) and managing distributed filesystems and cloud object storage.
• Active, or the ability to obtain, a Secret security clearance.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

We are seeking a technical Senior AI/ML Software Engineer to lead the architecture and deployment of large-scale distributed data systems and advanced machine learning pipelines. In this role, you will design infrastructure capable of analyzing high-throughput data streams. You will bridge the gap between High-Performance Computing (HPC) and modern AI applications, optimizing complex inference workloads for specialized hardware accelerators while guiding cross-functional engineering teams.

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 $207,000-$300,000 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

• Architect and operate advanced data synthesis pipelines and AI-based retrieval applications.
• Manage petabyte-scale data ingestion and synchronization across compute environments, including local storage, cloud backends, and on-prem resources.
• Optimize highly parallel numerical operations and ML inference algorithms for specialized hardware accelerators.
• Lead technical direction and provide engineering mentorship for groups developing complex production software systems.
• Implement rigorous data life-cycle policies to ensure system resilience, data integrity, and fault recovery at scale.