Machine Learning Hardware Architect, Google Cloud

• 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.

Minimum qualifications:

• 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.

Preferred qualifications:

• 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.

• 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.