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Senior GPU Architect, Deep Learning
Не указаноПолная занятостьHardware 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.
Обязанности
- 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
Требования
- 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
Будет плюсом
- 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