Staff Software Engineer, Embedded Systems/Firmware, AI2 Platforms Hardware Health

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.

• 8 years of experience in software development.

• 5 years of experience working with embedded operating systems.

• 3 years of experience with software design and architecture.

• Experience with C/C++, Python, and embedded software development for microcontrollers or embedded systems.

• Experience with software development in resource and power constrained micro-controller environments, and embedded RTOS and Linux environments.

Preferred qualifications:

• 10 years of experience in system-level software development.

• Experience in embedded software and diagnostics development.

• Experience in software development for hardware validation and quality assurance.

• Experience collecting and analyzing data to improve system reliability.

• Experience building tools, automation, and tests to deliver systems.

• Experience developing, debugging, and testing within the Linux kernel.

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.

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

The AI2 Platforms Hardware Health group focuses on ensuring the function and reliability of our hardware ecosystem. At Google, we explore leading-edge hardware solutions, and this team is involved in the early design and setting of product requirements. We focus on training and inference acceleration, leading-edge storage solutions, compute servers with Intel, AMD, ARM, and other processors, high-speed, low-latency networking and interfaces, and more that we can’t talk about. We are involved in every new product that is used in our data centers (DeepMind, Cloud services, Search, YouTube, etc.).

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities:

• Develop tools and diagnostics in support of machine learning system health manufacturing and verification, performance characterization, and ongoing reliability.

• Develop software that executes in thousands of systems in parallel, and develop dashboards to analyze the results.

• Enable testing and decision-making on hardware and software design and deployment.

• Support emerging technologies, including systems for artificial intelligence, machine learning, video transcoding, security, and image processing.

• Design next-generation platforms powering AI to push computation limits.