Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development or 1 year of experience with an advanced degree in an industry setting.
- 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures or algorithms.
- Experience developing and deploying applications utilizing Generative AI models, frameworks, or APIs.
- Experience building and deploying machine learning models or infrastructure in production environments.
- Experience building, developing, or operating network infrastructure or distributed systems.
About the jobGoogle's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
US: $147000 - $211000 (USD) 15% bonus target bonus equity benefits
Learn more about benefits at Google .
Responsibilities - Design and maintain TPU supercomputer software across multiple stack layers, ranging from daemons on host machines to network routing rules embedded directly into the TPUs.
- Develop and manage control software on specialized machines and distributed infrastructure to support the operation of massive collections of networked hardware.
- Implement robust systems to monitor, deploy, qualify, and service supercomputing systems, ensuring they remain reliable and performant at scale.
- Engineer software solutions for the reliable scale-out and scale-up of accelerators, specifically tailored to meet the needs of massive-scale machine learning applications.
- Architect and build software to optimally interconnect TPUs, enabling efficient execution of data parallelism algorithms like ring all-reduce.
