Software Engineer, Machine Learning Compilers, Silicon

2 weeks ago

Bellevue WA US Google Full time

Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience in software development, and with data structures/algorithms.
  • 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
  • Experience with compilers (e.g., optimization, parallelization, etc.).
Preferred qualifications:
  • Master's degree or PhD in Engineering, Computer Science, or a related technical field.
  • 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  • Experience in optimizing machine learning models for inference.
  • Experience with Compiler development in the context of accelerator-based architectures.
  • Experience in MLIR and/or LLVM.

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

Edge TPU is a family of embedded Machine Learning (ML) accelerators focused toward a broad set of applications, from smartphones to self-driving cars to data center applications. The compiler team is responsible for analysis, optimization, and compilation of ML models focused on the Edge TPU.

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.

The US base salary range for this full-time position is $189,000-$284,000 + 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 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 benefits at Google .

Responsibilities

  • Work as part of the Edge TPU 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 Edge TPU.
  • Work on efficient mapping of generative AI models and other key workloads into Edge TPU instructions through the compiler.
  • Work with Edge TPU architects to design future accelerators, hardware/software interface, and co-optimizations of the next generation Edge TPU architectures.
  • Collaborate with ML model developers, researchers, and Edge TPU hardware/software teams and product managers to accelerate the transition from research ideas to optimal user experiences running on the Edge TPU.

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