> Markdown version of [/jobs/ext/2034516-senior-staff-performance-codesign-engineer-tpu](https://www.wearedevelopers.com/jobs/ext/2034516-senior-staff-performance-codesign-engineer-tpu). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Staff Performance Codesign Engineer, TPU

- **Company:** Google LLC
- **Location:** Sunnyvale, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Engineering, Systems Analysis, Machine Learning, Software Systems, Google Cloud, Information Technology, Hardware Acceleration, Machine Learning Operations
- **Published:** August 12, 2026
- **Apply:** https://us.experteer.com/career/view-jobs/senior-staff-performance-codesign-engineer-tpu-sunnyvale-tx-usa-58909058

## About the Role

compute, memory bandwidth, and inter-chip networks * Manage cross-functional partnerships across hardware engineering, compiler development, and ML research to influence strategy and production transition * Contribute to TPU architecture, verification, and integration within AI/ML-driven systems * Shape long-term architectural roadmap for future ML training and serving capabilities * Promote integration of foundational ML research with custom silicon for high performance and efficiency * Support Google Cloud and global users by delivering scalable AI hardware technologies Tasks * Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or related field; 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 Key requirements * bonus target * equity * benefits

## Description

Experteer Overview In this role you will define and drive the technical roadmap and architecture for AI hardware-software systems, enabling high-performance training and inference for large ML models. You will bridge research, software, and hardware teams to push model architecture and accelerator innovations at scale. You will architect next-gen simulation frameworks and performance models to rigorously evaluate micro-architectural decisions. You contribute to shaping Google's TPU strategy and production-ready, power-efficient accelerators in a hyperscale environment. Compensation / Benefits * Define and drive the technical roadmap and architecture for hardware/software stack * Serve as technical liaison between research, software, and hardware teams to steer model architecture innovations * Architect and oversee development of configurable simulation frameworks and cycle-accurate performance models * Advocate system-level performance analysis across distributed ML systems and balance compute, memory bandwidth, and inter-chip networks * Manage cross-functional partnerships across hardware engineering, compiler development, and ML research to influence strategy and production transition * Contribute to TPU architecture, verification, and integration within AI/ML-driven systems * Shape long-term architectural roadmap for future ML training and serving capabilities * Promote integration of foundational ML research with custom silicon for high performance and efficiency * Support Google Cloud and global users by delivering scalable AI hardware technologies Tasks * Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or related field; 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 Key requirements * bonus target * equity * benefits


## Related Videos

- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [The Cloud is Calling: Answer with In-Demand Skills](https://www.wearedevelopers.com/videos/945-the-cloud-is-calling-answer-with-in-demand-skills)
- [30 Golden Rules of Deep Learning Performance](https://www.wearedevelopers.com/videos/11-30-golden-rules-of-deep-learning-performance)
- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [Cloud Run- the rise of serverless and containerization](https://www.wearedevelopers.com/videos/106-cloud-run-the-rise-of-serverless-and-containerization)
- [Detecting Money Laundering with AI](https://www.wearedevelopers.com/videos/111-detecting-money-laundering-with-ai)

## Related Articles

- [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence)
- [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers)
- [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)