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.
As a Software Engineering Manager in the Google Distributed Cloud (GDC) and Sovereign Cloud organization, you will lead teams bringing Google’s infrastructure and AI to secure on-premises and edge environments. You will architect orchestration platforms that define the industry’s approach to distributed computing. Your teams will solve challenges across the hardware footprints, including accelerator fleets, to optimize performance and efficiency. By driving sovereign initiatives, you ensure advanced capabilities operate within complex regulated frameworks globally. We seek a leader to manage distributed engineering teams and deploy, large-scale projects at the intersection of global cloud infrastructure and modern security.
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.
The US base salary range for this full-time position is $207,000-$300,000 + 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
- Set team priorities supporting GDC and Sovereign Cloud goals, specifically for GenAI and air-gapped runtimes. Align direction and decision-making across distributed teams to ensure high-velocity platform execution.
- Scale and mentor an engineering organization specialized in container orchestration and AI infrastructure. Provide continuous coaching to help individuals navigate technical complexities and career growth within highly regulated environments.
- Architect the mid-term technical road map for GKE AI, optimizing for accelerator (GPU/TPU) efficiency and cost-per-token. Evolve systems to meet future infrastructure needs for autonomous, self-managing cloud operations in restricted settings.
- Design and vet complex system architectures, solving ambiguous problems balancing performance and sovereignty. Advocate the engineering best practices, style, testability, and efficiency to ensure, secure, and auditable deployments.
Qualifications
Minimum qualifications:
- Bachelor’s degree, or equivalent practical experience.
- 8 years of experience in software development.
- 3 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
- 3 years of experience in a technical leadership role.
- 2 years of experience in a people management or team leadership role.
- 2 years of experience with container orchestration platforms (e.g., Kubernetes) and machine learning infrastructure, including hardware accelerators (e.g., GPUs, TPUs) or large-scale AI model serving platforms.
Preferred qualifications:
- Master's degree or PhD in Computer Science or a related technical field.
- 3 years of experience working in a complex, matrixed organization.
- 3 years of experience building or managing large-scale AI/ML infrastructure, including hardware accelerators (GPUs/TPUs) or platforms for Generative AI inference.
- 3 years of experience with Sovereign Cloud architecture, air-gapped environments, or navigating complex regulatory compliance standards (e.g., FedRAMP, StateRAMP, or ITIL).
- 3 years of experience managing the development and deployment of Kubernetes-based solutions within hybrid, on-premises, or edge computing environments.