Experience in building developer tools for computing accelerators such as APU, NPU, GPU and DSP.

Experience in power and performance optimizations.

Experience with low-level and low intrusiveness developer tooling concepts such as profiling, instrumentation, API tracing, hardware tracing.

Understanding of hardware, especially hardware that provides a degree of parallelism.

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.

As a Software Engineer you will work on developing toolings as part of SoC Software Development Kit (SDK) for the Tensor TPU to accelerate complex machine learning models running on custom hardware accelerators. Along with your technical expertise, you will manage project priorities, deadlines and deliverables.

This opportunity sits at the intersection of Machine Learning (ML) accelerators, optimization and deployment of ML models, embedded systems, and will be on the critical path of customers looking to productionize their models on consumer hardware.

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

• Build tools that compliments compiler to efficiently map ML models (with a particular focus on always-on computing use cases) to the hardware ISA.
  
• Design and implement new ways to gather useful performance metrics from hardware or software stack.
  
• Design and implement tools that can correlate performance data at a ML graph level or logical hardware level.
  
• Build performance analysis tools for simulators or new hardware.
  
• Show users how to use our tools to analyze, debug and improve latency, accuracy, and power through codelabs, documentation, and tutorials.