Software Engineering Manager, GPU Reliability
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Oct 12, 2026. Google scores B on the Alion truth index.
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.
The Cloud GPU team drives AI innovation by building and maintaining Google Cloud’s industry-leading GPU fleet and AI platform. We empower sophisticated training and inference customers by delivering computational resources. Owning the entire lifecycle of GPU offerings from launching new architectures to ensuring optimal reliability for workloads. We operate at the intersection of hardware, software, and applied AI. In this role, you will collaborate with cross-functional partners to build the foundational infrastructure fueling AI advancements across global industries. You will contribute to the future of AI and enable breakthrough solutions for a rapidly evolving customer base.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.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
- Lead, mentor, and grow a team of software engineers, fostering a collaborative culture and supporting career development.
- Execute technical roadmaps for the GPU ecosystem, anticipating market shifts to advance Google Cloud's AI infrastructure.
- Oversee the accelerator solution lifecycle, driving root-cause analysis and resolving production issues to guarantee fleet reliability.
- Collaborate with customers as a technical advocate to resolve critical issues and translate feedback into platform enhancements.
- Author and review technical specifications to maintain architectural consistency, high code quality, and long-term platform maintainability.
Qualifications
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience in leading technical project strategy, Machine Learning (ML) design, and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 years of experience in a technical leadership role.
- 2 years of experience in a people management, supervision/team leadership role.
Preferred qualifications:
- Master's degree or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.
- Experience with data science, data modeling, large-scale data processing, and reliability metrics.
- Experience in runtimes, Firmware, GPU drivers, embedded systems, embedded processors, and real time systems.