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Senior Applied Deep Learning Research Scientist, Efficiency
NVIDIA
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Santa Clara, United States
Location
Santa Clara
Posted
June 03, 2026
Commute
Local Area
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Job Description
We are now looking for an Applied Deep Learning Research Scientist, Efficiency!
Join our ADLR β Efficiency team to make deep learning faster and consume less energy! Our team influences the next-generation hardware to make AI more efficient; we work on the Nemotron series of models to make our state-of-the-art deep learning models the most efficient OSS models out there; and we develop new technology, software and algorithms to optimize neural networks for training and deployment. Topics include quantization/sparsity/optimizers/reinforcement learning, efficient architectures and pre-training. Our team is located inside the Nemotron pre-training team, collaborating across the company to make Nvidia GPUs the most efficient AI platform possible. Our work quite literally reaches the entire deep learning world. We are looking for applied researchers that want to develop new technologies for efficiency - and who want to understand the βwhyβ in efficiency, getting to the root-c...
Join our ADLR β Efficiency team to make deep learning faster and consume less energy! Our team influences the next-generation hardware to make AI more efficient; we work on the Nemotron series of models to make our state-of-the-art deep learning models the most efficient OSS models out there; and we develop new technology, software and algorithms to optimize neural networks for training and deployment. Topics include quantization/sparsity/optimizers/reinforcement learning, efficient architectures and pre-training. Our team is located inside the Nemotron pre-training team, collaborating across the company to make Nvidia GPUs the most efficient AI platform possible. Our work quite literally reaches the entire deep learning world. We are looking for applied researchers that want to develop new technologies for efficiency - and who want to understand the βwhyβ in efficiency, getting to the root-c...