Engineering Manager, GPU Reliability, Accelerators

Minimum qualifications

• Bachelor’s degree, or equivalent practical experience.
• 8 years of experience programming in C++, Java, Python, Kotlin or Go.
• 5 years of experience with software architecture and embedded systems.
• 3 years of experience in a technical leadership role.
• 2 years of experience in a people management or team leadership role.
• Experience with GPU programming, systems reliability and computer architecture.

Preferred qualifications

• Master's degree or PhD in Computer Science or related technical field.
• 3 years of experience working in a complex, matrixed organization.
• Experience in organizational design to consolidate task forces, steering platforms from New Product Introduction to General Availability.
• Experience operating in a Cloud environment and partnering with strategic customers to improve infrastructure.
• Experience leveraging AI platforms, Generative AI Agents, and distributed systems for software development.
• Strong background in GPU Systems Operations, designing automated diagnostic frameworks and telemetry for hardware and software faults.

About the job

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way. With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally. This is your chance to lead at the absolute cutting-edge of AI infrastructure. In this role, you will manage a team responsible for the reliability of the world's largest GPU supercomputer systems, the massive "brains" that play a vital role in driving Google's AI advancements. You will have the unique opportunity to navigate transformative technologies, from low-level GPU system software reliability to working with AI agents, positioning yourself at the forefront of the AI infrastructure domain. The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. We're behind Google's groundbreaking innovations, empowering the development of AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Compensation and benefits

Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . Responsibilities Architect the GPU Reliability Systems Operations and Tooling SW organization to transition from NPI-specific task forces into a centralized organization. Drive the GPU support strategy by developing a roadmap for fleet health reliability, capacity turn-up, and automated health management. Establish and enforce Service Level Objectives and Service Level Indicators for GPU Pod availability and performance. Sponsor automation and toil reduction efforts by driving the development of advanced telemetry and debugging tooling. Manage high-severity escalations for critical hardware and software issues, leading incident response and blameless post-mortems.