Staff Software Engineer, ML Compilers
Job Summary
Job Description
This Staff Software Engineer role focuses on ML compilers for Google's EdgeTPU. Responsibilities include analyzing and improving compiler quality and performance, developing parallelization and scheduling algorithms, collaborating with architects on future accelerator designs, efficiently mapping generative AI models to EdgeTPU instructions, and working with product managers and researchers to identify key ML trends. The ideal candidate will have extensive experience in software development, testing, and launching software products, along with expertise in compilers, ML model development, and C++. Experience with MLIR or LLVM is highly preferred. The role involves close collaboration with ML model developers, researchers, and hardware/software teams to accelerate the transition of research to user experiences on the EdgeTPU.
Must have:
- 8+ years software development experience
- 5+ years testing & launching software
- Compiler experience (parallelization)
- C++ programming
- ML model development expertise
Good to have:
- PhD in CS or related field
- MLIR/LLVM experience
- Accelerator-based compiler development
- Optimizing ML models for inference
- Hardware-software codesign
3 skills required
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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