Software Engineer, ML Compilers, Silicon Engineering

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. As a Software Engineer, you will work on developing ML compilers for the Tensor TPU to accelerate Generative AI and other complex machine learning models running on custom hardware accelerators. Along with your technical expertise, you will manage project priorities, deadlines and deliverables. The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world. Build compilers and tools that efficiently map ML models (with a particular focus on always-on computing use cases) to the hardware ISA. Propose and implement fixes to the compiler to address these issues in collaboration with other compiler engineers. Triage and root-cause correctness and performance issues encountered while enabling ML models on EdgeTPU. Interact closely with model owners to influence their model architectures to make them run efficiently on the EdgeTPU. Minimum qualifications: Bachelor’s degree or equivalent practical experience. 2 years of experience with ML compilers or compilers in general. Experience with system software. Preferred qualifications: Master's degree or PhD in Computer Science or related technical fields. 2 years of experience with data structures and algorithms. Experience in optimizing ML model inference on device. Experience in power and performance optimizations. Good understanding of hardware, especially hardware that provides a high degree of parallelism.