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 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.
As a part of the Workspace AI SRE team, your mission is to safely and responsibly enable rapid iteration of high-quality AI (e.g. Gemini) services and features using common infrastructure within Workspace. You will partner with development teams to ensure new AI-powered features for products like Gmail, Docs, Meet, and more are reliable, scalable, and efficient. As Workspace AI products move fast to stay competitive, SRE plays a critical role in building the necessary automation and safety guardrails.
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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google .
Own the architecture and design of the inference and training AI infrastructure from SRE side, ensuring it is reliable, scalable, cost effective and performant, while working closely with senior technical leads in the development teams.
Drive AI-first development to help the team leapfrog in its current efforts, serving as the AI advocate who introduce new and novel ways of working.
Lead the on-call response to production incidents, driving blameless postmortems and ensuring preventative measures are implemented.
Engage broadly with SRE leaders across other product teams to ensure the best scalable solutions for all of Google, acting as the conduit to bring solutions in and take them across Google.
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.
4 years of experience leading projects, and providing technical leadership.
3 years of experience in designing, analyzing, and troubleshooting distributed systems.
Experience with machine learning/AI in a software development environment.
Preferred qualifications:
Master's degree in Computer Science or Engineering.
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.
4 years of experience leading projects, and providing technical leadership.
3 years of experience in designing, analyzing, and troubleshooting distributed systems.
Experience with machine learning/AI in a software development environment.