Senior Applied Deep Learning Research Scientist, Efficiency

About This Role

Join the ADLR – Efficiency team to enhance deep learning speed and reduce energy consumption. Influence next-generation hardware and optimize neural networks for training and deployment.

Responsibilities

• Research 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
• 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
• 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

• PhD degree in AI, computer science, computer engineering, math or a related field or equivalent experience
• 5+ years of relevant industrial research experience

Nice to Have

• 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

* Required skills

Benefits