Senior Applied Deep Learning Research Scientist, Efficiency

About the role

We are now looking for an Applied Deep Learning Research Scientist, Efficiency! Join our ADLR – Efficiency team to make deep learning faster and consume less energy!

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.

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.

Qualifications

• Experience in quantization, pruning, numerics and efficient architectures.
• A background in computer architecture.
• Experience with GPU computing, kernels, CUDA programming and/or performance analysis.

Skills

• 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

• Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
• The base salary range is 192,000 USD - 304,750 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
• You will also be eligible for equity and benefits.

Pay

• Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
• The base salary range is 192,000 USD - 304,750 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

Schedule

• Not specified.