Senior Staff Software Engineer, AI/ML Diagnostic Systems and Hardware Coverage

Google•Published 9 hours ago•First seen 9 hours ago

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.

The Platforms Infrastructure Engineering Core Systems and Capacity Assurance team is responsible for delivering the tools and systems that manage the state of a machine throughout it's service lifetime while part of the Google Fleet. We are also responsible for the diagnostic tests, and repair strategies to ensure that a machine is healthy and ready to serve. This includes software installation, hardware health testing, and machine management. We are responsible for the validation of machine health at the time of manufacture and also when it is deployed in the data center.

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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

Serve as the technical authority driving the convergence of Artificial Intelligence, silicon/hardware design analysis, and systems engineering.
Architect and build generative AI tools, foundation model skills, and agentic workflows by leveraging complex ASIC architectural specifications, system schematics, software contracts, and historical test execution telemetry.
Transform how hardware diagnostic software is created, optimized, and deployed across Google’s next-generation compute, accelerator (TPU/GPU), network, and storage infrastructure.
Empower Diagnostic Software Engineers to achieve comprehensive test coverage across all hardware components, interfaces, and functional blocks.
Optimize test execution time and pinpoint optimal repair actions using advanced artificial intelligence systems.

Minimum qualifications:

Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
7 years of experience leading technical project strategy, ML design, and end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
5 years of experience with design and architecture; and testing/launching software products.
5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
Experience in designing, implementing and troubleshooting hardware diagnostics.

Preferred qualifications:

Master’s degree in Computer Science, Electrical/Computer Engineering, Artificial Intelligence, or equivalent practical experience.
10 years of professional software engineering and machine learning experience, including technical leadership on infrastructure systems.
8 years of experience with data structures/algorithms.
Experience building production machine learning systems, agentic architectures, LLM pipelines, or deep learning models applied to system telemetry.
Advanced experience in C++ or Rust, along with experience using machine learning frameworks and data processing tools.
Knowledge of computer architecture, modern ASIC/SoC functional blocks, high-speed interfaces, and hardware failure mechanisms.