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.

Based in Seattle and London, we manage Google Cloud Engine (GCE) AI/ML workloads and the critical infrastructure powering them.

As a Site Reliability Engineer (SREs) you will deliver a seamless customer experience. You will act as a first responder for AI workload health and customer-facing issues. You will build and support capabilities for managing ML workloads and influence architecture, standards, and operational methods for AI services. You will develop advanced monitoring and alerting to improve GCE visibility and collaborate with development teams on novel, emerging technologies.

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 $174,000-$252,000 + 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

• Act as a first responder for AI workload health and customer-facing issues. Build and support capabilities for managing ML workloads.

• Influence architecture, standards, and operational methods for AI services.

• Develop advanced monitoring and alerting to improve GCE visibility.

• Collaborate with development teams on novel, emerging technologies.

• Bridge the gap between the infrastructure and AI.

Minimum qualifications

• Bachelor's degree or equivalent practical experience.

• 5 years of experience working on cloud distributed systems that demand scalability, reliability, throughput and low latency.

• 3 years of experience coding with one or more programming languages (e.g., Java, C/C++, Python).

• 2 years of experience with debugging and troubleshooting software issues.

Preferred qualifications

• Master's degree in a technical field or equivalent practical experience.

• Experience designing, analyzing and troubleshooting large-scale distributed systems.

• Experience designing and developing software oriented towards systems or network automation.

• Understanding of Unix/Linux operating systems.

• Ability to debug, optimize code, and to automate routine tasks.

• Excellent problem-solving and communication skills.