Applied Deep Learning Research Scientist, Sparsity

Details

We are seeking DL researchers and/or computer architects to help future accelerators and neural networks take advantage of sparsity. This is an applied research position, with the goal of developing sparsity-related techniques (in isolation and combined with reduced precision) and transferring the most promising to SW and HW products. Work entails investigations, publication, and collaboration with researchers and practitioners (internal and external to NVIDIA).

Responsibilities

• 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.

Requirements

• 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.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you!