Staff Software Engineer, AI/ML, Google Public Sector

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.
• - 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.

Minimum qualifications:

Preferred qualifications:

Qualifications

• 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.