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.

The ML Accelerator SRE team mission is to deliver an exceptional ML compute infrastructure for all users. We ensure that all ML accelerators are fully and appropriately supported as part of the TI and Cloud Compute platforms and that all ML jobs run efficiently, safely and reliably. We support both the hardware and the low level services that provide ML as an IaaS.

The US base salary range for this full-time position is $147,000-$211,000 + 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

• Design/develop new features to help us support ML operations across Technical Infrastructure (TI) and Google Cloud Platform (GCP).

• Participate in tier 2 on-call support for ML accelerator and platform issues.

• Identify and improve our operational experience and help us reduce toil and reduce incident impact.

• Define and improve metrics and Service Level Objectives (SLOs) for the operations of the ML fleet.

• Work with Platform Infrastructure Engineering (PIE), Cloud, and SRE stakeholders to understand and reduce risks to upcoming accelerator launches.

Minimum qualifications

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

• 2 years of experience with software development in one or more programming languages (e.g., Golang).

• Experience with debugging and troubleshooting software issues.

Preferred qualifications

• Master's degree in Computer Science or Engineering, a related field, or equivalent practical experience.

• 4 years of experience designing, analyzing, and troubleshooting large-scale distributed systems.

• 2 years of experience with data structures and algorithms.

• Experience with Machine Learning infrastructure.

• Experience in statistical analysis to identify trends and root causes in production data.