Job description

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. In this role, 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. 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: $192000 - $278000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . • Drive the definition and optimization of the hardware/software stack to enable performant training and serving of large ML models. • Collaborate with research and modeling teams to innovate on model architectures, focusing on scaling, quality, and their direct impact on hardware performance. • Lead the development of configurable architectural simulators and cycle-accurate performance models to quantify microarchitectural optimizations and evaluate architectural decisions. • Conduct system-level performance analysis across highly distributed ML systems, innovating new methodologies to balance compute, memory bandwidth, and inter-chip network requirements. • Engage with partners across hardware design, compiler development, and ML research to transition architectural innovations from concept to production. • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience. • 10 years of experience in computer architecture, chip architecture, or hardware-software co-design. • Experience developing systems for performance modeling, simulation, or system analysis. Minimum qualifications: • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience. • 10 years of experience in computer architecture, chip architecture, or hardware-software co-design. • Experience developing systems for performance modeling, simulation, or system analysis. Preferred qualifications: • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture. • Experience architecting hardware solutions or performance optimizations for ML training and inference. • Experience with deep learning frameworks such as TensorFlow or PyTorch. • Deep understanding of ML trends, business drivers, and the software ecosystem. • Ability to engage and collaborate with hardware designers, software architects, and ML researchers.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Drive the definition and optimization of the hardware/software stack to enable performant training and serving of large ML models.
Collaborate with research and modeling teams to innovate on model architectures, focusing on scaling, quality, and their direct impact on hardware performance.
Lead the development of configurable architectural simulators and cycle-accurate performance models to quantify microarchitectural optimizations and evaluate architectural decisions.
Conduct system-level performance analysis across highly distributed ML systems, innovating new methodologies to balance compute, memory bandwidth, and inter-chip network requirements.
Engage with partners across hardware design, compiler development, and ML research to transition architectural innovations from concept to production.
Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
10 years of experience in computer architecture, chip architecture, or hardware-software co-design.
Experience developing systems for performance modeling, simulation, or system analysis.
Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
10 years of experience in computer architecture, chip architecture, or hardware-software co-design.
Experience developing systems for performance modeling, simulation, or system analysis.

Nice-to-haves

US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits
Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
Experience architecting hardware solutions or performance optimizations for ML training and inference.
Experience with deep learning frameworks such as TensorFlow or PyTorch.
Deep understanding of ML trends, business drivers, and the software ecosystem.
Ability to engage and collaborate with hardware designers, software architects, and ML researchers.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learning

Platform: google
Effort: medium