Senior Software Engineer, AI, Tensor Processing Unit, Infrastructure
at Alphabet
Posted 13 hours ago
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Join Google as a Senior Software Engineer focused on AI infrastructure and Tensor Processing Unit (TPU) integration. You will design and implement software to bootstrap compute or storage components, including OS image deployment and node configuration, and build Cloud Compute software to connect TPU clusters with cloud systems. You will partner with TPU hardware and software teams to develop and validate scalable ML infrastructure, mentor engineers, and help drive high-quality delivery across cross-functional teams.
Senior Software Engineer, AI, Tensor Processing Unit, Infrastructure
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience in testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 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 selection making), ML infrastructure, or specialization in a related ML field.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
Preferred qualifications:
- Experience with software development in C++ and cloud, or data center compute technologies.
- Experience with AI infrastructure software, reference OS, and storage systems.
- Experience in machine learning, and with hardware and software stacks.
- Experience in leading technical initiatives across cross-functional teams with excellent communication skills.
- Excellent problem-solving skills, with the ability to work in a changing collaborative environments.
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.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
- Design and implement software to bootstrap compute or storage components, including Operating System (OS) image deployment and node-specific configuration.
- Build and integrate Cloud Compute software to establish an Artificial Intelligence (AI) infrastructure connecting Tensor Processing Unit (TPU) clusters and cloud systems.
- Partner with TPU hardware and software teams to design, develop, and validate systems for standalone deployment.
- Collaborate with cross-functional leadership to drive quality project delivery and shape the future of Machine Learning (ML) infrastructure.
- Provide technical guidance and mentorship to engineers, promoting professional growth and team development.
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