Software Engineer, ML Compilers, Silicon

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

Job Summary

Job Description

Google is seeking a Software Engineer to develop ML compilers for Tensor TPUs, accelerating Generative AI and other machine learning models on custom hardware. Responsibilities include building compilers and tools that map ML models to hardware ISA, evaluating parallelization strategies (performance, power, energy, memory), collaborating with ML researchers and hardware engineers, and managing project priorities. The ideal candidate possesses experience in C++, data structures, algorithms, and ideally, compiler experience and knowledge of parallel hardware. The role focuses on always-on computing use cases and contributes to Google's mission of making information universally accessible.
Must have:
  • C++ or other programming languages
  • Data structures and algorithms
  • Build ML compilers and tools
  • Evaluate parallelization strategies
  • Collaborate with researchers and engineers
Good to have:
  • Master's degree in CS/CE
  • Power and performance optimizations
  • Compiler experience
  • Parallel hardware knowledge

Job Details

Minimum qualifications:

  • Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience.
  • 1 year of experience in C++ or other programming languages.
  • Experience in data structures and algorithms.

Preferred qualifications:

  • Master's degree in Computer Science or Computer Engineering, or a related field.
  • Experience in power and performance optimizations.
  • Experience with compilers.
  • Knowledge of hardware that provides a high degree of parallelism.

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.

Google's mission is to organize the world's information and make it universally accessible and useful. The team combines Google AI, Software, and Hardware to create helpful experiences. In this role, you will research, design, and develop new technologies and hardware to make computing faster. The team's goal is to make people's lives better through technology.

As a Software Engineer, you will work on developing ML compilers for the Tensor TPU to accelerate Generative AI and other machine learning models running on custom hardware accelerators. You will manage project priorities, deadlines and deliverables.

Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.

Responsibilities

  • Build compilers and tools that map ML models, with a focus on always-on computing use cases, to the hardware ISA.
  • Evaluate various trade-offs of different parallelization strategies such as performance, power, energy, and memory consumption.
  • Collaborate with machine learning researchers to improve the domain-specific compiler.
  • Collaborate with hardware engineers to evolve future accelerators.

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