Details zum Jobangebot
NVIDIA's technology is at the heart of the AI revolution, touching people across the planet by powering everything from self-driving cars, robotics, and voice-powered intelligent assistants. Academic and commercial groups around the world are using NVIDIA GPUs to revolutionize deep learning and data science, and to power data centers.We are looking for a Deep Learning Engineer with strong research skills to join the DL Algorithms team that is developing new efficient DL architectures. In this role you will interact with the scientific community creating algorithms to accelerate the training and inference of Large Language Models and Diffusion Models.
What You’ll Be Doing
- Invent and develop model optimization algorithms based on Neural Architecture Search, Pruning, Knowledge Distillation, Quantization, Conditional Computation, Model fine-tuning, etc.
- Work in a dynamic, applied team of researchers and engineers
- Work on large-scale multi-node ML models
- Publish research papers and implement the results in Nvidia products
- Collaborate with academia
- M.Sc. plus 4 years of commercial experience in Computer Science, Artificial Intelligence, Applied Math, or related field
- Machine learning fundamentals (linear algebra, probability theory, optimization, supervised/unsupervised/self-supervised ML, etc.)
- Hands-on experience with designing Deep Learning models (Transformers, Diffusion Models, Convolutional Neural Networks etc.)
- Programming skills (Python, C/C++), algorithms & data structures, debugging, performance analysis, and design skills.
- Strong experience with deep learning frameworks such as PyTorch or TensorFlow
- Ability to work independently and handle your own work effort
- Good communication and documentation habits
- Ph.D. degree or equivalent experience in Computer Science, Artificial Intelligence, Applied Math, or related field
- Strong track of publications on Deep Learning in leading international conferences/journals
- Experience with ML model optimization techniques such as Neural Architecture Search, Pruning, Distillation, Quantization, Conditional Computation, etc.
- Knowledge of CPU and/or GPU architectures in the context of ML algorithms
