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.
An SRE Manager on the Shopping Site Reliability Engineering (SRE) team, specializing in Shopping AI, you will drive reliability and efficiency within AI-powered systems in the Shopping domain. Your role requires software engineering expertise, understanding of production systems, and a passion for leveraging AI to enhance the shopping experience.
You will drive your team to design, implement, and maintain AI-driven infrastructure, ensuring its scalability, reliability, and performance. You will align your team's work with priorities in Shopping development and broader Ads and Commerce SRE organization.
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
- Lead a team of Software/Systems Engineers on projects for users and be directly responsible for uptime.
- Own end-to-end availability and performance of key services and build automation to prevent problem recurrence. Automate response to all non-exceptional service conditions.
- Lead by example, mentor the team and establish credibility through quality technical execution.
- Design, write and deliver software to improve the availability, scalability, latency and efficiency of Google's services.
- Manage on-call rotations across continents, using a follow-the-sun model.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
- 8 years of experience with data structures and algorithms.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience managing people or teams, leading projects, and designing, analyzing, and troubleshooting distributed systems.
- Experience building and developing large-scale infrastructure or distributed systems.
- Experience in Machine Learning Infrastructure.
Preferred qualifications:
- Master's degree in Computer Science or Engineering.