Software Engineer, Developer Tools, Silicon

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.

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

Google's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.

Responsibilities

• Develop software tools and scripts to diagnose, analyze system issues of Google custom silicon. Develop a dongle-less debugger via USB or any existing peripheral interface.
• Present the analytics in both CLI (Command Line Interface), and GUI (Graphical Interface).
• Apply machine learning on the analytics to pinpoint the likely root causes and the corresponding self-recovery methods.
• Engage cross-functional with Google teams to build APIs, tools, and user interfaces.Enable test automation to collect the analytics automatically in development test farms.
• Review hardware designs, prototype software-based hardware mocks, and engage in hardware/software (HW/SW) co-design efforts to get the full understanding of the debug or performance features implemented in the tools.