info_outline XIn accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:Health, dental, vision, life, disability insurance Retirement Benefits: 401(k) with company match Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks Baby Bonding Leave: 18 weeks Holidays: 13 paid days per year. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Kirkland, WA, USA. 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, and 3 years of experience with software design and architecture. 5 years of experience with Machine Learning compilers (optimization, parallelization, etc.). 5 years of experience with relevant ML design and ML infrastructure (e.g., model deployment, model evaluation, etc.). Preferred qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field. Experience in optimizing ML models for inference. Experience compiling for heterogeneous architectures across IPs, including CPU, GPU, and NPUs. Experience with hardware-software co-design. Experience in MLIR or Low Level Virtual Machine (LLVM). Experience in compiler development, particularly in the context of accelerator-based architectures, vector instruction optimizations, or vectorizing compilers. 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. EdgeTPU is a family of embedded machine learning (ML) accelerators that aim toward a broad set of applications. The compiler team is responsible for analysis, optimization, and compilation of ML models focusing EdgeTPU. 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Work as part of the EdgeTPU compiler team, building ML compilers for EdgeTPU hardware and analyzing and improving the compiler quality and performance on optimization decisions, correctness and compilation time. Work with and extend ML authoring frameworks, including JAX, Pytorch to compile ML models for the EdgeTPU. Work with ML runtime systems to deploy optimized ML models on the EdgeTPU. Work with EdgeTPU architects to design the Hardware/Software (HW/SW) interface, and co-optimizations between CPU, GPU, and TPU. 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.