Nvidia Deep Learning Performance Architect - New College Graduate 

United States, California 

87744903

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 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:

• A Masters Degree or PhD in Computer Science, Electrical Engineering, Computer Engineering or related relevant field (or equivalent experience)

• Strong foundation in machine learning and deep learning

• Experience with performance modeling, architecture simulation, profiling, analysis, and visualizations

• Strong programming skills in Python, C, C++

• Experience with automated CI/CD pipelines, job orchestration, and building continuous delivery capabilities in a dynamic development environment.

Ways to stand out from the crowd:

• Background with GPU Computing and parallel programming models such as CUDA

• Experience with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, JAX, TensorRT)

• Experience with the architecture of or workload analysis on other DL accelerators