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# Machine Learning Hardware Architect, Google Cloud

- **Company:** Google LLC
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Salary:** $163,000.0 - $236,000.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Computer Engineering, Hardware Design, Python (Programming Language), Machine Learning, CPU Design, Tensorflow, Software Engineering, Google Cloud, Pytorch, Deep Learning, Information Technology
- **Published:** September 18, 2026
- **Apply:** https://dejobs.org/x/x/AE64C189792F4B8C805AA6630BF86189/job/

## About the Role

Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area., * Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.

 * 5 years of experience in computer architecture, chip architecture, IP architecture, co-design, performance analysis, or hardware design.
 * Experience in developing software systems in C++ or Python., * Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on Computer Architecture, or a related field.
 * 8 years of experience in computer architecture, chip architecture, IP architecture, co-design, performance analysis, or hardware design.
 * Experience in processor design or accelerator designs and mapping ML models to hardware architectures.
 * Experience with deep learning frameworks including TensorFlow and PyTorch.
 * Knowledge of Machine Learning market, technological and business trends, software ecosystem, and emerging applications.
 * Knowledge of hardware/software stack for deep learning accelerators.

## Description

* Create differentiated architectural innovations for Google's semiconductor Tensor Processing Unit (TPU) roadmap.
 * Evaluate the power, performance, and cost of prospective architecture and subsystems.
 * Collaborate with partners in Hardware Design, Software, Compiler, ML Model and Research teams for hardware/software co-design.
 * Work on Machine Learning (ML) workload characterization and benchmarking.
 * Develop architecture for differentiating features on next generation TPUs.


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