Job Description

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 leading projects and designing, analyzing, and troubleshooting distributed systems.

Preferred qualifications

• Experience working in computing, distributed systems, storage, or networking.
• Ability to debug, optimize code, and to automate routine tasks.
• Excellent problem-solving, with effective verbal and written communication skills.
• Eagerness to learn and apply new technologies in the rapidly evolving AI/ML space.
• Interest to deep dive to understand root causes and complex system interactions.
• Expertise in designing, analyzing, and troubleshooting large-scale distributed systems.

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 AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Poland: zł480000 - zł492000 (PLN) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

Responsibilities

• Drive the end-to-end reliability, scalability, and performance of large-scale Models and Artificial Intelligence (AI) workloads on Vertex AI.
• Architect and operate fleet management automation to securely manage, monitor, and upgrade one of the world's largest Google Cloud Platform (GCP) and Google Kubernetes Engine (GKE) environments.
• Optimize low-latency, high-throughput AI serving infrastructure, maximizing the efficiency, observability, and availability of accelerator resources.
• Partner with development teams on system design, launch reviews, and capacity planning to evolve data and control planes before services go live.
• Scale systems sustainably through self-healing software, eliminating toil and driving architectural changes that enhance deployment velocity and customer platform resilience.