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.
Vertex AI is a key pillar to the Cloud AI's mission is to responsibly deliver AI that enables industries and organizations to transform and solve real-world problems through a single unified artificial intelligence platform that enables building, deploying, and scaling ML models faster with either pre-trained and custom tooling.
We are Tier 1 on-call for Vertex AI Online Prediction and Large Language Models, Gen AI and are planning to continue to extend support to the most critical services within the Vertex AI suite.
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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google .
Develop scalable and sustainable system architecture and designs for products, services and enhancements.
Defend performance of critical services in alignment with customer expectations and SLOs.
Own and define strategy and set direction and establish roadmaps for Vertex AI services to increase reliability, efficiency and ultimately feature velocity.
Resolve outages or service disruptions and help design solutions to ensure systems are protected from similar classes of problems in the future.
Collaborate with development counterparts to incorporate and deliver enhancements to systems resulting in improved reliability, scalability and or performance.
Minimum qualifications:
Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
8 years of experience with software development in one or more programming languages.
2 years of experience in Site Reliability Engineering, Production Engineering, or Systems Engineering roles managing large - scale infrastructure.
3 years of experience designing, analyzing, and troubleshooting distributed systems.
3 years of experience in machine learning infrastructure.
Preferred qualifications:
Master's degree in Computer Science or Engineering.
Experience enhancing and supporting large production systems on compute infrastructure.
Experience with networking, capacity and performance.
Experience in large-scale systems, architecture design and complex system integrations or migrations.
Experience supporting a Tier 1 rotation.
Expertise in SRE production principles and best practices.
Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
8 years of experience with software development in one or more programming languages.
2 years of experience in Site Reliability Engineering, Production Engineering, or Systems Engineering roles managing large - scale infrastructure.
3 years of experience designing, analyzing, and troubleshooting distributed systems.
3 years of experience in machine learning infrastructure.