Senior Applied Deep Learning Research Scientist, Efficiency
Core
Developing new algorithms, numeric formats, and architecture improvements to optimize neural networks for training and deployment, making deep learning faster and more energy-efficient.
Role type
Senior Applied Deep Learning Research Scientist (Efficiency)
Builds
Optimized neural network architectures, quantization/sparsity techniques, and efficient training/inference algorithms for the Nemotron series of models.
Domain
AI/ML, Deep Learning, Computer Architecture, GPU Computing
Deliverable
production ML models
Required skills
PhD in AI/CS/Engineering/Math, 5+ years industrial research experience, expertise in neural network architectures and optimizers, LLM training knowledge, Python fluency, large-scale experiment execution, publication record
Preferred skills
Quantization, pruning, numerics, efficient architectures, computer architecture background, GPU computing, CUDA programming, performance analysis
Technologies
Python, CUDA, LLM training frameworks, inference engines
Responsibilities
Research low-bit number representations and pruning effects on accuracy, innovate new efficiency algorithms, run large-scale deep learning experiments, collaborate with hardware and software teams
Seniority
Senior, hands-on IC