What We Need To See BS, MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, or equivalent experience. 12+ years of relevant industry experience in GPU architecture, computer architecture, or other parallel processing architectures. Strong background in hardware architecture and microarchitecture. Experience defining and evaluating architectural features with solid understanding of 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 the ability to drive technical work across distributed, interdisciplinary teams. Ways To Stand Out From The Crowd Deep understanding of modern GPU architecture and the interaction between hardware and AI workloads. Experience with memory subsystem architecture, interconnects, coherence, scheduling, or execution pipelines. Experience with pre-silicon performance studies, workload characterization, and architectural correlation. Familiarity with training and inference behavior for large-scale deep learning models. Experience with silicon bring-up, debug, or post-silicon analysis. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, autonomous, and love a challenge, consider joining our GPU Architecture team and help us build the next generation of AI computing platforms. , , JR2019902
