Software Engineering Manager, GPU AI Infrastructure

Minimum qualifications:

• Bachelor’s degree, or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 3 years of experience with embedded operating systems.
• 3 years of experience in a technical leadership role.
• 2 years of experience in a people management or team leadership role.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• Experience in developing software that interacts with hardware: e.g. embedded systems, drivers, system software.
• Experience in system integration and NPI (New Product Introduction).
• Experience with Machine Learning (ML) concepts and GPUs.

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.

We develop system software that enables state-of-the art GPU-based AI/ML supercomputers in Google’s data centers, and thus empowering cutting edge AI/ML innovations both for Google and Cloud customers.

In this role, you will design and develop the systems software and networking technologies for accelerator products containing Graphics Processing Units, Video Transcoders, and various combinations of those accelerators. The execution covers the areas of system software integration, manufacturing testing, machine bring-up, data center deployment, resource management, security, virtualization and finally platform telemetry, monitoring and control.

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 the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge 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.

Responsibilities

• Manage, grow and lead a team with a various technical portfolio that interacts with a large set of Google Services and Cloud teams, and cross-functional stakeholders.
• Develop, integrate, test, deploy and debug the system software for GPU and other accelerator systems.
• Interact and integrate with a variety of software components including: board and chip firmware, linux kernel drivers, high speed interconnect bus firmware, hardware design, Google data center server management and monitoring stack, etc.
• Collaborate with hardware, manufacturing, data center operations team, cloud engineering, and other external partners to plan and execute the programs end-to-end, including product development, vendor engagement, manufacturing, and productivity improvements.
• Define technical goal and roadmaps that bridge team priorities with organizational goals. Drive growth through clear role expectations, consistent coaching, and proactive feedback.