About The Position
NVIDIA is searching for an outstanding researcher working on efficient deep learning to join the deep learning efficiency research team. We are passionate about research that pushes boundaries but also has impact in the real world. We are particularly excited about methods for post-training model optimization (pruning, quantization, NAS), efficient architecture design, adaptive/dynamic inference, resource-efficient training and finetuning, and so forth. You will work within an amazing and collaborative research team that consistently publishes at the top venues in computer vision and machine learning. Our existing expertise includes computer vision, deep learning, generative models, and so forth. Your contributions have the chance to create real impact on our products.
Requirements
- Completing or recently completed a Ph.D. in Computer Science/Engineering, Electrical Engineering, etc., or have equivalent research experience.
- Excellent knowledge of theory and practice of computer vision methods, as well as deep learning.
- Experience with large language models and large vision-language models is required.
- Excellent programming skills in Python and PyTorch; C++ and parallel programming (e.g., CUDA) is a plus.
- Hands-on experience with large-scale model training including data preparation and model parallelization (tensor and pipeline) is required.
- Outstanding research track record.
- Excellent communications skills.
Nice To Haves
- Background in pruning, quantization, NAS, efficient backbones, and so on, is a plus.
Responsibilities
- Research, design and implement novel methods for efficient deep learning.
- Publish original research.
- Collaborate with other team members and teams.
- Mentor interns.
- Speak at conferences and events.
- Work with product groups to transfer technology.
- Collaborate with external researchers.
Benefits
- You will also be eligible for equity and benefits
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
