{"schemaVersion":"jobsearcher.job.v1","id":"f6c39748e34c6d7a0ed405ca","url":"https://jobsearcher.com/jobs/f6c39748e34c6d7a0ed405ca","canonicalUrl":"https://jobsearcher.com/jobs/f6c39748e34c6d7a0ed405ca","title":"Performance Co-Design Engineer, Google Cloud TPU","description":"Minimum qualifications: Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.\n10 years of experience in computer architecture, chip architecture, or hardware-software co-design.\nExperience developing systems for performance modeling, simulation, or system analysis.\nPreferred qualifications: Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.\nExperience architecting hardware solutions or performance optimizations for large-scale ML training and inference.\nExperience with deep learning frameworks such as TensorFlow or PyTorch.\nDeep understanding of ML trends, business drivers, and the software ecosystem.\nAbility to engage and collaborate with hardware designers, software architects, and ML researchers.\nAbout the job: In this role, you'll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.\nAs a Staff Co-Design Engineer on the TPU Architecture team, you will act as a key technical anchor bridging the gap between model architecture innovation and next-generation hardware design. Operating cross-functionally across AI research and engineering, you will help shape the architectural roadmap for our future machine learning serving and training capabilities. You will drive the integration of Machine Learning (ML) research such as the training and serving of massive foundation models with advanced silicon architectures to deliver industry-leading, high-performance, and power-efficient accelerators.\nThe 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.\nWe'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.\nIndividual pay is determined by factors including job-related skills, experience, and relevant education or training.\nUS: $192000 - $278000 (USD) + 20% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities: Drive the definition and optimization of the hardware/software stack to enable performant training and serving of large ML models.\nCollaborate with research and modeling teams to innovate on model architectures, focusing on scaling, quality, and their direct impact on hardware performance.\nLead the development of configurable architectural simulators and cycle-accurate performance models to quantify microarchitectural optimizations and evaluate architectural decisions.\nConduct system-level performance analysis across highly distributed ML systems, innovating new methodologies to balance compute, memory bandwidth, and inter-chip network requirements.\nEngage with partners across hardware design, compiler development, and ML research to transition architectural innovations from concept to production.\n\n#J-18808-Ljbffr","company":"Socket","rawCompany":"socket","city":"Sunnyvale","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-14T03:11:23.538Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"17-2061.00","title":"Computer Hardware Engineers","slug":"computer-hardware-engineers"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"334111","title":"Electronic Computer Manufacturing","slug":"electronic-computer-manufacturing"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Performance Co-Design Engineer, Google Cloud TPU","description":"Minimum qualifications: Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.\n10 years of experience in computer architecture, chip architecture, or hardware-software co-design.\nExperience developing systems for performance modeling, simulation, or system analysis.\nPreferred qualifications: Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.\nExperience architecting hardware solutions or performance optimizations for large-scale ML training and inference.\nExperience with deep learning frameworks such as TensorFlow or PyTorch.\nDeep understanding of ML trends, business drivers, and the software ecosystem.\nAbility to engage and collaborate with hardware designers, software architects, and ML researchers.\nAbout the job: In this role, you'll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.\nAs a Staff Co-Design Engineer on the TPU Architecture team, you will act as a key technical anchor bridging the gap between model architecture innovation and next-generation hardware design. Operating cross-functionally across AI research and engineering, you will help shape the architectural roadmap for our future machine learning serving and training capabilities. You will drive the integration of Machine Learning (ML) research such as the training and serving of massive foundation models with advanced silicon architectures to deliver industry-leading, high-performance, and power-efficient accelerators.\nThe 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.\nWe'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.\nIndividual pay is determined by factors including job-related skills, experience, and relevant education or training.\nUS: $192000 - $278000 (USD) + 20% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities: Drive the definition and optimization of the hardware/software stack to enable performant training and serving of large ML models.\nCollaborate with research and modeling teams to innovate on model architectures, focusing on scaling, quality, and their direct impact on hardware performance.\nLead the development of configurable architectural simulators and cycle-accurate performance models to quantify microarchitectural optimizations and evaluate architectural decisions.\nConduct system-level performance analysis across highly distributed ML systems, innovating new methodologies to balance compute, memory bandwidth, and inter-chip network requirements.\nEngage with partners across hardware design, compiler development, and ML research to transition architectural innovations from concept to production.\n\n#J-18808-Ljbffr","datePosted":"2026-08-14T03:11:23.538Z","dateModified":"2026-08-14T03:11:23.538Z","hiringOrganization":{"@type":"Organization","name":"Socket","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Sunnyvale","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"f6c39748e34c6d7a0ed405ca"},"url":"https://jobsearcher.com/jobs/f6c39748e34c6d7a0ed405ca"}}