Machine Learning Hardware Architect, Hardware, Software Co-Design, Google Cloud

• Define and drive the technical roadmap and architecture for the hardware/software stack to ensure exceptional performance for ML models. Act as the technical liaison across research, software, and hardware teams, steering model architecture innovation to maximize scaling, quality, and hardware efficiency.
• Architect next-generation configurable simulation frameworks and performance models, setting the organizational standard for evaluating complex microarchitectural decisions. Drive high-stakes choices regarding Power, Performance, Area (PPA) and buildability for future chip and system architectures, expertly balancing long-term technological trends with strict product delivery timelines.
• Guide system-level performance analysis across highly distributed ML systems, innovating new methodologies to optimize and balance compute, memory bandwidth, and inter-chip network requirements. Their leadership will directly shape the future of high-performance AI infrastructure and hardware-software co-design.
• Manage cross-functional partnerships across hardware, compiler development and ML teams.

Minimum qualifications:

• 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 architecting and developing software systems in C++ or Python for performance modeling, simulation, or system analysis.

Preferred qualifications:

• Master’s degree or PhD in Electrical Engineering, Computer Engineering, or Computer Science with an emphasis on computer architecture.
• Experience as a lead architect managing multi-generational hardware solutions or performance optimizations for massive-scale ML training and inference.
• Experience in semiconductor technologies, industry trends, and the future trajectory of process, memory, interconnects, and packaging.
• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and deep understanding of their underlying execution models.

• 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 architecting and developing software systems in C++ or Python for performance modeling, simulation, or system analysis.