Software Engineer III, TPU Backend, Complier

3 Months ago • 2-6 Years • Artificial Intelligence • Full Stack Development • Backend Development • $136,000 PA - $200,000 PA

Job Summary

Job Description

As a Software Engineer III on the TPU Backend Compiler team, you will be responsible for developing and optimizing the software that powers Google's Tensor Processing Units (TPUs). You will work on a variety of tasks, including designing and implementing new compiler features, optimizing existing code for performance, and collaborating with other engineers to ensure the smooth operation of the TPU backend. This role is critical to the success of Google's AI efforts, as it ensures that TPUs can be used to train and deploy machine learning models at scale.
Must have:
  • Bachelor's degree or equivalent practical experience
  • 2 years of experience with data structures or algorithms
  • 2 years of experience with software development in C++
  • 2 years of experience in performance optimization
  • 2 years of experience working with compilers
  • 1 year of experience with ML infrastructure
Good to have:
  • Master's degree or PhD in Computer Science or related technical fields
  • Experience developing accessible technologies

Job Details

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience with data structures or algorithms.
  • 2 years of experience with software development in C++.
  • 2 years of experience in performance optimization.
  • 2 years of experience working with compliers.
  • 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

  • Master's degree or PhD in Computer Science or related technical fields.
  • Experience developing accessible technologies.

About the job

Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. 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 Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. 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 US base salary range for this full-time position is $136,000-$200,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about .

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.
  • Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.

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