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.
The Cloud GPU team is central to AI innovation, dedicated to building and maintaining an industry-leading GPU fleet and AI Platform. We're responsible for the entire lifecycle of GPU offerings within Google Cloud, from the initial launch of new GPU families to ensuring their optimal reliability and operational excellence for AI workloads.
Our team grows at the intersection of hardware, software, data science and applied AI, constantly pushing the boundaries of what's possible in accelerated computing. We collaborate closely with internal and external partners to deliver the foundational infrastructure that fuels advancements in artificial intelligence across different industries.
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.
The US base salary range for this full-time position is $262,000-$365,000 + 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
- Execute technical roadmaps for the GPU ecosystem around GPU resilience, anticipating market shifts to keep Google Cloud at the forefront of AI infrastructure.
- Collaborate with engineering teams to integrate new GPU architectures into Google Compute Engine (GCE) for rapid workload availability.
- Grow and lead engineering talent in the GPU space.
- Oversee the lifecycle of accelerator solutions, guaranteeing consistent performance and stability for different user applications.
- Serve as a technical advocate during critical issues, collaborating directly with customers to resolve challenges and translating their feedback into platform enhancements.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience with software development.
- 7 years of experience leading technical project strategy, ML design, and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience in a people management or team/technical leadership role.
- 5 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 10 years of experience with software engineering.
- 5 years of experience working in a complex, matrixed organization.
- Experience running cloud infrastructure (e.g. GPU).