AI Computing Architect
About the role
Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. NVIDIA’s GPUs runs AI algorithms, simulating human intelligence, and act as the brains of computers, robots and self-driving cars that can perceive and understand the world.
Increasingly known as “the AI computing company”, NVIDIA wants you. Come, join our AI Computing Architecture team, where you can help build real-time, cost-effective computing platforms driving our success in this exciting and rapidly growing field. We are seeking outstanding Performance Analysis Architects with a background in the following to help analyze and develop the next generation of architectures that accelerate AI and high-performance computing applications.
What you’ll be doing:
Develop innovative architectures to extend the state of the art in deep learning performance and efficiency.
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.
Prototype key deep learning and data analytics algorithms and applications.
Actively collaborate with software, product and research teams to guide the direction of deep-learning.
What we need to see:
BS or higher degree in a relevant technical field (CS, EE, CE, Math, etc.) with 3+ years of work experience.
Strong programming skills in Python, C, C++.
Strong background in computer architecture.
Experience with performance modeling, architecture simulation, profiling, and analysis.
Strong foundation in machine learning and deep learning.
Ways to stand out from the crowd:
Experience with GPU Computing and parallel programming models such as CUDA and OpenCL.
Background with the architecture of or workload analysis on other deep learning accelerators.
Experience with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, Tensorflow, TensorRT).
Experience with open-source AI compilers (OpenAI Triton, MLIR, TVM, XLA, etc.).
Questions about this role
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