Minimum qualifications: PhD degree in Computer Science, a related technical field, or equivalent practical experience. Experience coding in one of the following programming languages including but not limited to: C, C++, Java, or Python. Experience in one or more of the following: architecting or developing distributed systems, concurrency, multi-threading, or synchronization. Preferred qualifications: Experience with performance, reliability, systems data analysis, visualization tools, or debugging. Experience in code and system health, diagnosis and resolution, and software test engineering. Research experience in algorithms, architecture, artificial intelligence, compilers, database, data mining, distributed systems, machine learning, networking, or systems. Experience with performance, reliability, systems data analysis, visualization tools, architecture, compilers, database, data mining, networking or systems. Experience with Unix/Linux, Kernel development, microcontrollers, SoC, device drivers, hardware, power management, ARM processors, performance optimization, file systems, bootloading, firmware, x86 assembly, system BIOS, or hardware/software integration. About the job Google Cloud's software engineers build the next-generation technologies that transform how billions of users connect, explore, and interact with information and each other. We're looking for engineers who bring fresh ideas across areas like information retrieval, distributed computing, large-scale system design, networking, data storage, security, AI, and natural language processing—the list keeps growing. As a Software Engineer, you’ll work on projects critical to Google Cloud’s evolving needs, with the flexibility to move between teams and initiatives as both you and our business grow. You'll be empowered to think like an owner, proactively identifying customer needs, taking action, and driving innovation. We value engineers who are versatile, display leadership, and eagerly handle challenges across the full stack. Within Google Cloud, the Machine Learning, Systems, and Cloud AI (MSCA) organization creates category-defining AI/ML capabilities built on Google’s frameworks, infrastructure, and services. We design and manage the software, hardware, and ML systems infrastructure that power Google services like Search and YouTube, and Google Cloud products. As a PhD Software Engineer in MSCA, your research expertise will help solve real-world problems at a massive scale. You'll collaborate on innovative projects in areas such as AI, ML, and distributed systems, contributing to products used by billions. With thousands of PhDs across Google, your academic background will be part of a strong community of researchers and engineers shaping the future of technology. We prioritize security, efficiency, and reliability in everything we do, from developing TPUs to operating one of the world’s largest networks, while shaping the future of hyperscale computing. 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. Responsibilities Write product or system development code. Participate in, or lead design reviews with peers and stakeholders to decide on available technologies. Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency). Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality. Lead and collaborate on team projects to carry out design, analysis, and development across the stack using your research expertise. Study, diagnose and resolve complex technical modeling and systems issues by analyzing the sources of the issues and the impact on quality.