Software Engineer III, Diagnostics, Tools, Google Cloud Platform

1 Month ago • 2 Years + • Artificial Intelligence • Research & Development

Job Summary

Job Description

This Software Engineer III role focuses on developing diagnostic tools and enhancing the reliability of Google's machine learning and AI acceleration platforms. Responsibilities include creating tools for system health verification, performance characterization, parallel system execution software, and analytical dashboards. The position involves collaborating with software, firmware, and hardware teams to design, implement, and debug systems, impacting hardware and software design and deployment decisions. The work contributes directly to the quality and performance of Google Cloud Platform's diagnostic tools and utilities.
Must have:
  • Bachelor's degree in Computer Science or equivalent
  • 2 years software development experience (Python, C, C++)
  • 2 years experience with data structures/algorithms
  • Develop diagnostic tools for system health
  • Develop software for parallel system execution
Good to have:
  • Master's or PhD in Computer Science
  • Experience with performance analysis, diagnostics, debugging
  • Experience with C/C++, Python, and Golang
  • Experience developing on Linux
  • Experience in system health diagnosis and resolution

Job Details


Minimum qualifications:

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

Preferred qualifications:

  • Master's degree or PhD in Computer Science or related technical field.
  • Experience in software development with performance, systems data analysis, diagnostics tools, and debugging.
  • Experience in system health, diagnosis and resolution, and software test engineering.
  • Experience with C/C++, Python, and Golang.
  • Experience developing on Linux.

About the job

Google'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.

The team explores hardware solutions and is involved in design and setting product requirements. The team focuses on artificial intelligence and machine learning acceleration, storage solutions, compute servers, other processors, high-speed low-latency networking, and interfaces.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

Responsibilities

  • Develop tools and diagnostics in support of system health verification, performance characterization, and on-going reliability of machine learning and AI acceleration platforms.
  • Develop software that executes multiple systems in parallel, and develop dashboards to analyze the results.
  • Enable the testing and decision making on hardware and software design and deployment.
  • Enhance the quality, performance processes, or coverage of the diagnostic tool or utility of the Google Cloud platform.
  • Collaborate with Google Software, Firmware, and Hardware teams to design, plan, implement, and debug.

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