Join a pivotal team at Google Cloud, specializing in advanced AI/ML-tailored storage infrastructure. We are at the forefront of developing high-scale systems engineered to meet the evolving demands of AI and Machine Learning workloads. Our GCSFuse product aims to provide the fastest and most intuitive file client for AI/ML on Google Cloud Platform, experiencing significant growth. Future efforts will concentrate on hyper-scale performance, exceptional observability, and debuggability.

Google Cloud empowers organizations globally through digital transformation, delivering enterprise-grade solutions powered by cutting-edge technology. We provide tools that enable developers to build more sustainably, assisting customers across more than 200 countries to achieve growth and solve critical business challenges.

Take full ownership of your work, designing and implementing projects that span multiple quarters with minimal oversight. Cultivate expertise to become a subject matter expert in your assigned domain.

Identify and scope ambiguous problems, thoroughly researching potential solutions and technologies to recommend strategies that balance immediate and long-term needs.

Lead technical direction and set priorities for small engineering teams (3-4 members), performing high-complexity feature design and implementation. Deliver robust, production-ready code for storage solutions specifically addressing AI/ML workload requirements.

Serve as a key liaison for cross-functional partners, ensuring alignment on objectives and timelines with adjacent teams. Drive influence beyond your immediate role, impacting team members and collaborating with other departments.

A Bachelor’s degree or equivalent practical experience is required.

Possess a minimum of 5 years of software development experience in at least one programming language.

Demonstrate 3 years of experience in testing, maintaining, or launching software products, coupled with 1 year of experience in software design and architecture.

Experience with file systems and Linux kernel internals is essential.