Research and develop large language models, multimodal foundation models, and agentic AI systems across text, vision, speech, audio, and video.
Develop, train, fine-tune, and evaluate foundation models using supervised fine-tuning, instruction tuning, preference optimization, reinforcement learning, and related post-training techniques.
Develop and evaluate capabilities including reasoning, multimodal understanding, planning, tool use, conversational interaction, and multi-step task completion.
Build and curate high-quality datasets and evaluation benchmarks for model training, post-training, and performance analysis.
Prototype new model architectures, algorithms, training strategies, and inference techniques, and collaborate with researchers and engineers to scale successful ideas.
What we need to see
Pursuing an MS or PhD degree in Computer Science, Electrical Engineering, Artificial Intelligence, Applied Mathematics, or a related field.
Strong knowledge of machine learning and deep learning techniques, algorithms, and modern foundation model architectures, including Transformers.
Experience or exposure to large language models, vision-language models, or multimodal models, with an emphasis on reinforcement learning for agentic reasoning, planning, tool use, and coding.
Excellent programming skills in Python and experience with deep learning frameworks such as PyTorch, NeMo, Megatron-LM, TensorFlow, or related frameworks.
Experience developing, training, fine-tuning, or evaluating deep learning models, including familiarity with data processing, dataset development, experiment tracking, and model evaluation workflows.
Strong problem-solving skills and a demonstrated ability to rapidly prototype, experiment, and evaluate new ideas.
Ways to stand out from the crowd
Experience with large language models, multimodal foundation models, vision-language models, or agentic AI research and development.
Background with post-training techniques such as instruction tuning, preference optimization, RLHF, RLVR, GRPO, or related reinforcement learning methods.
Experience with multimodal reasoning across text, image, video, speech, or audio, including conversational or audio-language models.
Experience with reasoning, planning, tool use, function calling, or multi-step agentic systems.
Background with large-scale model training, distributed frameworks such as NeMo or Megatron-LM, GPU computing, or inference technologies such as vLLM or TensorRT-LLM.
NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most brilliant and talented people in the world working for us. If you're creative and autonomous, with a genuine passion for large language models, multimodal AI, and agentic systems, we want to hear from you!
Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The expected hourly rate for this position is 20 USD – 94 USD.
You will also be eligible for Intern benefits.
Applications for this job will be accepted at least until November 8, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
