The role
From Google's own posting.
Site Reliability Engineering (SRE) combines software and systems engineering to build and run large-scale, massively distributed, fault-tolerant systems. SRE ensures that Google's services—both our internally critical and our externally-visible systems—have reliability, uptime appropriate to users' needs and a fast rate of improvement. Additionally SRE’s will keep an ever-watchful eye on our systems capacity and performance.
Much of our software development focuses on optimizing existing systems, building infrastructure and eliminating work through automation. On the SRE team, you’ll have the opportunity to manage the complex challenges of scale which are unique to Google, while using your expertise in coding, algorithms, complexity analysis and large-scale system design.
SRE's culture of intellectual curiosity, problem solving and openness is key to its success. Our organization brings together people with a wide variety of backgrounds, experiences and perspectives. We encourage them to collaborate, think big and take risks in a blame-free environment. We promote self-direction to work on meaningful projects, while we also strive to create an environment that provides the support and mentorship needed to learn and grow.
To learn more: check out our books on Site Reliability Engineering or read a career profile about why a Software Engineer chose to join SRE.
Join Google’s Core AI Foundations SRE team! We build and scale the critical infrastructure—authorization, ML training storage, and RPC scheduling—powering products like Gemini, Workspace, NotebookLM, and Cloud.
You will manage and grow a North American SRE team. You will partner closely with development teams and our Sydney counterpart to run a follow-the-sun rotation and ensure exceptional reliability for Google’s frontier AI capabilities.
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
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 .
Hire, develop, and mentor a high-performing, SRE team to support career growth and team health.
Set team goals, prioritize resources, and define technical roadmaps aligned with partner teams and key stakeholders.
Guide the architecture and review of resilient, high-performance systems powering core AI infrastructure.
Drive incident response, maintain high reliability standards, and actively automate operational toil.
Partner across development teams to align technical direction while leveraging AI to accelerate team productivity.
Minimum qualifications:
Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
8 years of experience with software development in one or more programming languages (e.g., C++, Java, Python), or with data structures/algorithms.
3 years of experience in designing, analyzing, and troubleshooting large-scale distributed systems.
3 years of experience managing and growing engineering teams, including performance management and career development.
Preferred qualifications:
Experience managing distributed teams across multiple sites or timezones.
Experience developing long-term technical roadmaps, driving organizational change, and influencing cross-functional stakeholders (Dev, PM, Leadership).
Proven track record of hiring, mentoring, and leading high-performing Site Reliability Engineering or Software Engineering teams.
Systematic problem-solving and troubleshooting skills in complex, ambiguous software systems.
Passion for AI infrastructure and driving AI transformation within engineering workflows.
Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
8 years of experience with software development in one or more programming languages (e.g., C++, Java, Python), or with data structures/algorithms.
3 years of experience in designing, analyzing, and troubleshooting large-scale distributed systems.
3 years of experience managing and growing engineering teams, including performance management and career development.