Join Google Cloud as a Software Engineer III and contribute to groundbreaking distributed cloud technologies. You'll be at the forefront of innovation, developing systems that power how billions of users connect and interact with information. Our engineers tackle challenges at a massive scale, pushing the boundaries of information retrieval, distributed computing, system design, and more.

This role offers the chance to work on critical projects, with the flexibility to explore diverse teams and technologies as the business evolves. We value versatility, leadership, and a passion for solving complex, full-stack challenges in a fast-paced environment.

As a key member of the Google Distributed Cloud team, you will enhance the reliability and performance of high-performance computing assets. Your focus will be on building and scaling systems for hardware health monitoring, with a special emphasis on GPU diagnostics and infrastructure observability.

You will directly influence the uptime of large-scale server fleets by creating automated alerting mechanisms, sophisticated dashboards, and in-depth diagnostic tools. This includes designing and maintaining scalable software solutions for real-time monitoring of server infrastructure and GPU health, as well as developing and refining diagnostic suites to identify pre-failure indicators and performance bottlenecks.

Additionally, you'll build and optimize high-cardinality monitoring pipelines for actionable alerts, create automated recovery workflows, and collaborate with hardware vendors and site reliability teams to integrate low-level hardware telemetry into our monitoring platforms.

We are seeking candidates with a Bachelor’s degree or equivalent practical experience, coupled with at least 2 years of software development experience or 1 year with an advanced degree. A strong background in developing large-scale infrastructure, distributed systems, networks, or compute/storage/hardware architecture is essential.

Expertise in distributed systems, embedded systems, Linux, and monitoring is required. Preferred qualifications include a Master's degree or PhD in Computer Science or a related technical field, along with 2 years of experience in data structures and algorithms.

Experience with GPU Drivers, Embedded Systems, Kubernetes, and Docker will be advantageous.