Software Engineer, Cloud AI/ML, GPUs

Minimum Qualifications:

• Bachelor’s degree in Computer Science or equivalent practical experience.
• 1 year of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
• 1 year of experience with data structures or algorithms.

Preferred Qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• Experience with large-scale Cloud Infrastructure.
• Ability to communicate in English and Mandarin fluently to support client management in this region.

About The Job

The Cloud GPU team is central to AI innovation, dedicated to building and maintaining an industry-leading GPU fleet and AI Platform. Our core mission is to empower Google Cloud's training and inference customers by providing unparalleled computational resources. We're responsible for the entire lifecycle of GPU offerings within Google Cloud, from the initial launch of new GPU families to ensuring their optimal reliability and operational excellence for AI workloads.

As a Software Engineer, you will develop the next-generation technologies that change how users connect, explore, and interact with information and one another. You will bring ideas from all areas, including distributed computing, large-scale system design, networking and data storage, artificial intelligence, etc.

As a software engineer in the Cloud GPU team, you will focus on delivering tangible growth in the AI infrastructure space. The team works at the intersection of hardware, software, data science and applied AI, constantly pushing the boundaries of what's possible in accelerated computing. We collaborate closely with internal and external partners to deliver the foundational infrastructure that fuels advancements in artificial intelligence across industries.

Responsibilities

• Write product or system development code. 
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
• Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.