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 member of the TPU Machine Learning Supercomputer (MLSC) team, you will design and develop features to significantly improve the scalability and reliability of large-scale software across TPUs and other distributed networked hardware machines. Your work will span various layers of the software stack, from host daemons to network routing and distributed control software running across Google's internal and cloud infrastructure. You will also provide leadership to help formulate and drive software development plans for future supercomputer generations.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 behind Google's groundbreaking innovations, empowering the development of 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 - $301000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design, develop, test, deploy, and debug critical system software that enables TPU Machine Learning accelerators to function seamlessly.
- Develop advanced analytics and health management capabilities to effectively manage and optimize large-scale ML systems.
- Lead high-impact projects and steer successful delivery while ensuring alignment with broader team strategies.
- Provide technical guidance and mentorship to software engineers to foster their professional growth and development.
Qualifications
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience with software development in C++ or Go.
- 5 years of experience with large-scale infrastructure, distributed systems, or networks, as well as testing and launching software products.
- 3 years of experience with software design and architecture.
- Experience with operating systems, data structures, and algorithms.
- Experience developing, integrating, and testing system and user-space software (including tools, dashboards, and monitoring) for hardware accelerators or TPU systems.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
- Experience building backend software for high-performance computing (HPC) and machine learning (ML) applications, including knowledge of data analytics, ML architecture, and how common algorithms map to software/hardware operations.
- Understanding of highly distributed systems, control plane and management Software, and networking concepts.