Minimum qualifications:
- Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience in designing, analyzing, and troubleshooting large-scale distributed systems.
- 2 years of experience leading projects and providing technical leadership.
- Experience with Cloud Compute Platforms (e.g., Kubernetes, Cloud Functions).
- Experience in Site Reliability Engineering, System Design, and Distributed Computing.
Preferred qualifications:
- Master's degree in Computer Science or Engineering.
- Experience with Kubernetes, Google Cloud Platform (GCP), GKE Networking, and Istio.
- Passion for technology and technical depth to uncover root causes of complex technical problems and provide guidance on solving them.
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 Vertex Third-Party (3P) AI Platform SRE team is expanding, and we are looking for engineers to help us build and run the infrastructure for third-party AI workloads on Google Cloud's Vertex AI. We enable a seamless experience for third-party AI workloads on Vertex AI, focusing on reliability, performance, scalability, observability, and efficiency. We want to make Vertex AI the best place to run any AI model.
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 $166,000-$244,000 + 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.
- Manage and scale Google Kubernetes Engine (GKE) fleets (approximately 60 thousand clusters and growing), including specialized mega-clusters designed for AI/ML models.
- Optimize low-latency and high-throughput model serving across all layers of the platform stack to maximize performance for AI/ML inferences.
- Architect and maintain robust connectivity and interactions between the internal Borg-based control planes and the external GCP-based data planes in a complex hybrid environment.
- Implement and configure essential cloud technologies, including Istio for service mesh management, advanced load balancing, Google Compute Engine (GCE), and various GCP networking and security components.
