We are now looking for a Machine Learning Intern. Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time — the era of AI has begun. Image recognition and speech recognition — GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. The GPU started out as the engine for simulating human creativity, conjuring up the amazing virtual worlds of video games and Hollywood films.
Now, NVIDIA's GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for Deep Learning, and NVIDIA is increasingly known as "the AI computing company.
What you'll be doing:
• Support research and development of large language models and multimodal models.
• Work on model fine-tuning, parameter-efficient training, and architecture exploration.
• Assist with experiments, benchmarking, evaluation, and data analysis.
• Develop prototypes using NVIDIA AI platforms and GPU-accelerated tools.
• Collaborate with researchers and engineers on cutting-edge AI innovation projects.
• Explore opportunities for technical publications and research outputs.
What we need to see:
• Pursuing BS, MS, or PhD in Computer Science, AI, Data Science, Engineering, Mathematics, or related fields.
• Experience with machine learning / deep learning.
• Strong Python programming skills.
• Familiarity with PyTorch or TensorFlow.
• Good analytical and problem-solving skills.
• Good verbal and written communication skills in English.
Ways to Stand Out from the Crowd
• Experience with LLMs, VLMs, multimodal AI, NLP, or generative AI.
• Experience with distributed training or GPU computing.
• Interest in applied research and publications.