Senior Software Engineer, ML Infrastructure, Cloud AI

GoogleMountain View, CA
2d$174,000 - $252,000

About The Position

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.

Requirements

  • Bachelor’s degree or equivalent practical experience.
  • 5 years of experience with software development in C++.
  • 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
  • 3 years of experience with machine learning infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
  • Experience with high performance computing.

Nice To Haves

  • Master's degree or PhD in Computer Science or a related technical field.
  • Experience in compilers or runtimes.
  • Experience with low-level programming.
  • Experience in graphics processing unit (GPU) programming.

Responsibilities

  • Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  • Understand how accelerator compiler and runtimes interact at a high level.
  • Close infra gaps to help with end-to-end Machine Learning (ML) stack maturation (e.g., reduce a number of ways something is done, improve reproducibility, improve tooling, improve usability).
  • Develop and apply metrics to understand the problem you are solving and gage status/success as needed.
  • Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
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