Software Engineering Manager, AI and containers, Kubernetes Full-time

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with software development in one or more programming languages (e.g., Python, C, C , Java, JavaScript).
• 3 years of experience in a technical leadership role, overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
• 3 years of experience building and developing infrastructure or distributed systems.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 3 years of experience working in a complex, matrixed organization.
• Experience managing large ML platforms.
• Knowledge of Kubernetes, with a focus on AI inference workloads.
• Understanding of Public Cloud services, with a focus on Google Cloud Platform (GCP).

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.

Our mission is to make Google Kubernetes Engine (GKE) the default platform to deploy Generative AI Inference workloads. In this role, you will work in two domains, Containers and Generative AI.

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 $189,000-$284,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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 and communicate team priorities that support the broader organization’s goals. Align strategy, processes, and decision-making across teams.
• Set clear expectations based on their level and role aligned to the broader organization’s goals. Meet regularly to discuss performance and development and provide feedback and coaching.
• Develop the mid-term technical goal and roadmap within the scope of our teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
• Design and guide systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems.
• Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).