Job Summary:
NVIDIA is seeking an Applied Deep Learning Research Scientist to join their ADLR – Efficiency team. The role focuses on improving deep learning efficiency through innovative algorithms, research on neural network architectures, and collaboration across various teams to optimize AI technologies.
Responsibilities:
• Research of low-bit number representations and pruning and their effect on neural network inference and training accuracy. This includes requirements by the existing state of art neural networks, as well as co-design of future neural network architectures and optimizers.
• Innovate with new algorithms to make deep learning more efficient while retaining accuracy, and open-source or publish these algorithms for the world to use.
• Run large-scale deep learning experiments to prove out ideas and analyze the effects of efficiency improvements.
• Collaborate across the company with teams making the hardware, software and deep learning architectures.
Qualifications:
Required:
• PhD degree in AI, computer science, computer engineering, math or a related field or equivalent experience in some of the areas listed below can substitute for an advanced degree.
• 5+ years of relevant industrial research experience.
• Familiarity with state-of-art neural network architectures, optimizers and LLM training.
• Experience with modern DL training frameworks and/or inference engines.
• Fluency in Python, and solid coding/software-engineering practices.
• A proven track-record in publications and/or the ability to run large-scale experiments.
• A strong interest in neural network efficiency.
Preferred:
• Experience in quantization, pruning, numerics and efficient architectures.
• A background in computer architecture.
• Experience with GPU computing, kernels, CUDA programming and/or performance analysis.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.