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What you’ll be doing:
Analyze performance and power efficiency of the most important deep learning inference workloads
Understand and analyze the interplay of hardware and software architectures on forward-looking algorithms, programming models and applications
Identify and prototype opportunities for performance optimization
Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW
What we need to see:
MS or PhD in a relevant discipline (CS, EE, CE) or equivalent experience
6+ years of relevant work/research experience
Solid foundation in machine learning and deep learning
Excellent programming skills in Python, C, C++
Strong background in computer architecture
Experience with performance modeling, architecture simulation, profiling, and analysis
A track record of creative solutions to technical challenges
Ways to stand out from the crowd:
CUDA programming skills
Background with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, Tensorflow, TensorRT)
Experience with the architecture of or workload analysis on GPUs or other DL accelerators
You will also be eligible for equity and .
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