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What you’ll be doing:
Develop innovative architectures to extend the state of the art in deep learning performance and efficiency
Prototype key deep learning and data analytics algorithms and applications
Analyze performance, cost and power trade-offs by developing analytical models, simulators and test suites
Understand and analyze the interplay of hardware and software architectures on future algorithms, programming models and applications
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 Computer Science, Computer Engineering, Electrical Engineering or equivalent experience
5+ years of relevant work experience
Strong background in computer architecture and deep learning
Expert programming skills in Python, C, C++
Experience with performance and power modeling, architecture simulation, profiling, and analysis
Ways to stand out from the crowd:
Background in large scale distributed systems architectures and / or a background with GPU Computing and parallel programming models such as CUDA and OpenCL
Experience 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 other DL accelerators
You will also be eligible for equity and .
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