About the job

Site Reliability Engineering (SRE) combines software and systems engineering to build and run large-scale, massively distributed, fault-tolerant systems. SRE ensures that Google Cloud's services—both our internally critical and our externally-visible systems—have reliability, uptime appropriate to customer's 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 Cloud, 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.

Google Distributed Cloud air-gapped (GDCag) is an offering designed as a converged hardware and software solution for customers (e.g., Public Sector, Finance) with the most stringent data residency, security, compliance, and privacy requirements, operating completely disconnected from Google Cloud. GDCag includes dedicated hardware, operating systems, and a curated set of Google Cloud services, providing a consistent cloud experience in tightly controlled environments.

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

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

• Engage with and improve the entire lifecycle of services; from inception and design, through capacity planning and launch reviews, to deployment, operation and continual improvement.

• Solve large, ambiguous problems and drive solutions across Site Reliability Engineering (SRE) and Development teams where our reliability expertise lends us to be a team multiplier.

• Balance the need for a reliable system, efficient incident response and blameless postmortems with highly dynamic, customer priorities.

• Maintain services' long-term health by creating and monitoring service level objectives (SLOs), scaling systems and processes sustainably through mechanisms such as automation.

• Mentor and train team members on design, coding, and reliability best practices and grow knowledge of Gemini and Vertex AI within GDC SRE.

Minimum qualifications

• Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.

• 8 years of experience building or managing distributed systems or cloud infrastructure, with a focus on Kubernetes.

• 3 years of experience managing people or teams.

• Experience with systems design.

• Experience in site reliability engineering.

Preferred qualifications

• Master's degree in Computer Science or Engineering.

• 3 years of experience with Kubernetes.

• Experience in cloud computing environments, such as Google Compute Engine (GCE).