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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Staff Performance Codesign Engineer, TPU - **Company:** Google LLC - **Location:** Sunnyvale, CA, United States - **Experience:** Expert - **Salary:** $240,000.0 - $333,000.0 - **Contract:** Permanent contract - **Skills:** Computer Engineering, Systems Analysis, Machine Learning, Performance Tuning, Tensorflow, Pytorch, Deep Learning, Information Technology, Machine Learning Operations - **Published:** August 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=572154ba3bf73162 ## About the Role * Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience. * 12 years of experience in computer architecture, chip architecture, or hardware-software co-design. * Experience developing systems for performance modeling, simulation, or system analysis., * Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture. * Experience as a lead architect driving multi-generational hardware solutions or performance optimizations for massive-scale ML training and inference. * Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and their underlying execution models. * Knowledge of semiconductor trajectories, including process, memory, interconnects, and packaging. * Understanding of ML trends, business drivers, and the software ecosystem. * Ability to engage and align stakeholders, hardware designers, and the global ML research community. ## Description * Define and drive the technical roadmap and architecture for the hardware/software stack, ensuring unparalleled performance for the training and serving of large ML models. * Act as the technical liaison between advanced research, software, and hardware teams, steering model architecture innovation to maximize scaling, quality, and hardware efficiency. * Architect and oversee the development of next-generation configurable simulation frameworks and cycle-accurate performance models, setting the standard for how the organization evaluates complex micro-architectural decisions. * Advocate system-level performance analysis across highly distributed ML systems, innovating new methodologies to balance compute, memory bandwidth, and inter-chip network requirements. * Manage cross-functional partnerships across hardware engineering, compiler development, and ML research to influence broad organizational strategy and transition paradigm-shifting concepts into production. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form. ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [30 Golden Rules of Deep Learning Performance](https://www.wearedevelopers.com/videos/11-30-golden-rules-of-deep-learning-performance) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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