Nvidia Applied Deep Learning Research Scientist Sparsity 

United States, California 

9885880

What you'll be doing:

• Researching methods (adjusting model architectures, training procedures, etc.) to enable sparsity in neural networks while maintaining the quality of the results.

• Proposing hardware features to enable sparsity, studying their impact on DL acceleration and efficiency.

What we need to see:

• MS or PhD degree in computer science, computer engineering, electrical engineering or related field or equivalent experience.

• At least 3+ years of relevant work experience

• Experience with neural network pruning and sparsity, training networks for various tasks, exploring model architectures.

• Experience with modern DL training frameworks and/or inference engines.

• Background in computer architecture, performance analysis and optimization.

• Fluency in Python, C++, or ideally both.

• Experience with GPU computing, CUDA is not required but a big plus.

Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time. The era of AI has begun.