The Machine Learning Hardware Architect role at Google Cloud focuses on shaping the future of AI/ML hardware acceleration. The architect will work with cutting-edge TPU technology to drive innovations that enhance performance and efficiency in AI applications. This position involves collaboration with various teams to develop custom silicon solutions and optimize hardware designs under strict power and thermal constraints. The role is integral to advancing ML accelerator capabilities and ensuring seamless software integration.

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
• 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 is preferred.
• 8 years of experience in relevant fields is preferred.

• 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.

• Employees at Google are often offered benefits like comprehensive health insurance, 401(k) matching, and flexible work arrangements, among other benefits.