Senior GPU Architect, Deep Learning

Responsibilities

• Define and architect new GPU hardware features for future deep learning and parallel processing workloads.
• Drive microarchitectural exploration across key areas such as compute pipelines, memory hierarchy, data movement, synchronization, and performance efficiency.
• Analyze workload behavior and translate bottlenecks into clear architectural requirements and hardware feature proposals.
• Evaluate performance, power, area, complexity, and programmability tradeoffs for new architectural directions.
• Develop and use functional and performance models to study new features and refine the architecture before implementation.
• Work closely with RTL, design, verification, compiler, and software teams to ensure successful execution from architecture definition to productization.
• Create clear architecture specifications, validation plans, and success criteria for the features you define.
• Be ready to learn, dig deep, and work across the full stack when required - from workloads and models to RTL and silicon.
• What we need to see
• BS, MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, or equivalent experience.

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