Senior ML Compiler Engineer, Silicon

Minimum qualifications:

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
• 5 years of experience with Compilers
• 3 years of experience testing, maintaining, or launching software products, with 1 year of experience with software design and architecture.

Preferred qualifications:

• Master's degree or PhD in Computer Science, or a related technical field.
• Experience in power and performance optimizations.
• Experience with domain-specific compilers for machine learning.
• Knowledge of hardware that provides a degree of parallelism.

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 Machine Learning (ML) compilers for the Tensor TPU to accelerate Generative AI (GenAI)and other complex machine learning models running on custom hardware accelerators. Along with your technical expertise, you will manage project priorities, deadlines and deliverables.

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.

• Build compilers and tools that map ML models (with a particular focus on always-on computing use cases) to the hardware Instruction Set Architecture (ISA).
• Evaluate various trade-offs of different parallelization strategies such as performance, power, energy and memory consumption.
• Collaborate with machine learning researchers to improve the domain-specific compiler.
• Collaborate with hardware engineers to evolve future accelerators.
• Productize various ML workloads on the Pixel Edge TPU including Gemini Nano, Camera, Imaging and Speech ML models.