Hybrid Senior Applied Deep Learning Research Scientist, Efficiency

at NVIDIA

• Job title: Senior Applied Deep Learning Research Scientist, Efficiency
• Job type: Full Time
• Experience level: Senior
• Salary: $192,000 - $356,500 per year
• Degree requirement: Postgraduate Degree
• Tech skills: Python
• Location requirements: Hybrid, Santa Clara, United States

About the role

• Deep Learning Research Scientist at NVIDIA focusing on efficiency innovations for AI technologies. Collaborating across the company to enhance deep learning models and architectures.

Responsibilities

• Research of low-bit number representations and pruning and their effect on neural network inference and training accuracy.
• 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.

Requirements

• 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

Benefits

• Eligible for equity
• Benefits