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Senior GPU Architect, Deep Learning
Not specifiedFull-timeHardware EngineeringElectronics Manufacturing
Define future GPU architectures and hardware features for deep learning and accelerated computing, from workload analysis and modeling through validation and silicon readiness.
Responsibilities
- Define and architect GPU hardware features for deep learning and parallel processing workloads
- Explore microarchitecture across compute pipelines, memory hierarchy, data movement, synchronization, and performance efficiency
- Analyze workload behavior and translate bottlenecks into architectural requirements and hardware proposals
- Evaluate performance, power, area, complexity, and programmability tradeoffs
- Develop and use functional and performance models to refine architecture before implementation
- Collaborate with RTL, design, verification, compiler, and software teams through productization
- Create architecture specifications, validation plans, and success criteria
- Support work across workloads, models, RTL, and silicon
Requirements
- Bachelor’s, master’s, or doctoral degree in computer science, electrical engineering, or computer engineering, or equivalent experience
- At least 12 years of relevant industry experience in GPU architecture, computer architecture, or parallel processing architectures
- Strong background in hardware architecture and microarchitecture
- Experience defining and evaluating architectural features and assessing performance, power, and area tradeoffs
- Strong programming and scripting skills in C, C++, and Python
- Experience with architectural modeling, simulation, or performance analysis
- Background in parallel computing, memory systems, high-performance computing, or deep learning acceleration
- Strong communication skills and ability to drive technical work across distributed, interdisciplinary teams
Nice to have
- Deep understanding of modern GPU architecture and AI workloads
- Experience with memory subsystems, interconnects, coherence, scheduling, or execution pipelines
- Experience with pre-silicon performance studies, workload characterization, and architectural correlation
- Familiarity with training and inference behavior in large-scale deep learning models
- Experience with silicon bring-up, debugging, or post-silicon analysis