Staff Software Engineer, ML Compilers

9 Hours ago • 8 Years + • Research & Development

Job Summary

Job Description

As a Staff Software Engineer in the ML Compilers team at Google, you will work on optimizing ML workloads for EdgeTPU. Responsibilities include analyzing and improving compiler quality and performance, developing parallelization and scheduling algorithms, collaborating with hardware architects on future accelerator designs, and efficiently mapping generative AI models onto EdgeTPU instructions. You will work closely with ML model developers, researchers, and hardware/software teams to accelerate the transition from research to user experiences. This role requires expertise in compilers, ML model development, C++, and ideally experience with MLIR or LLVM. The ideal candidate will have experience optimizing ML models for inference and a strong understanding of hardware-software co-design.
Must have:
  • 8+ years software development experience
  • 5+ years in testing and launching software products
  • 3+ years in software design and architecture
  • Compiler experience (parallelization) or ML model development
  • C++ programming language proficiency
Good to have:
  • PhD in Computer Science
  • MLIR or LLVM experience
  • Compiler development for accelerator-based architectures
  • Optimizing ML models for inference
  • Hardware-software co-design experience

Job Details


Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience in software development.
  • 5 years of experience testing, and launching software products.
  • 3 years of experience with software design and architecture.
  • Experience with compilers (parallelization, etc.) or ML model development and C++ programming language.

Preferred qualifications:

  • PhD in Computer Science, or a related field.
  • Experience in Multi-Level Intermediate Representation (MLIR) or Low Level Virtual Machines (LLVM).
  • Experience with compiler development in the context of accelerator-based architectures.
  • Experience with optimizing ML models for inference.
  • Experience with hardware-software codesign.

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. 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

  • Work as part of the EdgeTPU compiler team, including analyzing and improving the compiler quality and performance on optimization decisions, correctness and compilation time.
  • Develop parallelization and scheduling algorithms to optimize compute and data movement costs to execute ML workloads on the EdgeTPU.
  • Work with EdgeTPU architects to design future accelerators, the hardware/software interface, and co-optimizations of the next generation EdgeTPU architectures.
  • Work on efficient mapping of generative AI models and other key workloads into EdgeTPU instructions through the compiler.
  • Work with Product Managers, Researchers in identifying key ML trends, future use cases, etc. Closely collaborate with ML model developers, researchers, and EdgeTPU hardware/software teams to accelerate the transition from research ideas to user experiences running on the EdgeTPU.

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