Software Engineer III, AI/ML, Technical Infrastructure

1 Month ago • 2-4 Years • Artificial Intelligence

Job Summary

Job Description

This Software Engineer III role at Google focuses on AI/ML technical infrastructure. Responsibilities include measuring and optimizing model performance on Google Cloud, identifying and resolving performance bottlenecks, developing training materials, contributing to product improvement through code development and testing, and conducting performance profiling and debugging. The ideal candidate possesses strong software development skills, expertise in data structures and algorithms, and experience with ML infrastructure. The role involves collaboration with internal teams and ensuring adherence to best practices. The position is within Google's MSCA organization, impacting Google services and Google Cloud customers.
Must have:
  • Bachelor's degree or equivalent experience
  • 2+ years software development experience
  • 2+ years experience with data structures/algorithms
  • Experience with ML infrastructure/performance
  • Proficient in Google Cloud Platform
Good to have:
  • Master's or PhD in Computer Science
  • 4+ years software development experience

Job Details


Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  • 2 years of experience with data structures or algorithms.

Preferred qualifications:

  • Master's degree or PhD in Computer Science or related technical fields.
  • 4 years of experience in software development using one or more programming languages, with expertise in data structures and algorithms.
  • 1 year of experience with ML infrastructure or performance.
  • Proficient in cloud services such as Compute, Storage, and Networking, particularly on Google Cloud Platform.

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 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

  • Measure and optimize AI/ML model performance on Google Cloud infrastructure.
  • Identify and resolve performance bottlenecks, collaborating with internal infrastructure teams to enhance support for demanding AI workloads as needed.
  • Develop and deliver high-quality training and demos for both customers and internal teams.
  • Contribute to ongoing product improvement by identifying bugs, recommending enhancements, and writing and testing production-quality code.
  • Conduct in-depth performance profiling, debugging, and troubleshooting of training and inference workloads, ensuring adherence to best practices through design and code reviews.

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