Nvidia Deep Learning Engineer - LLMs Diffusion Models 

Switzerland, Vaud 

797768010

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

What we need to see:

• 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-supervisedML, 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

Ways to stand out from the crowd:

• 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