Deep Learning Scientist Intern, LLM, Multimodal and Agentic AI - Summer 2027

NVIDIA•Santa Clara, CA

1d•$20 - $94

About The Position

NVIDIA is seeking a Deep Learning Scientist Intern to help research, design, and develop the next generation of large language models, multimodal foundation models, and agentic AI systems. The intern will apply machine learning and deep learning techniques across language, vision, speech, audio, and video to develop and improve foundation models that can understand, reason, plan, interact, and act.

Requirements

Pursuing an MS or PhD 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, particularly reinforcement learning for agentic reasoning, planning, tool use, and coding.
Excellent Python programming skills 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 demonstrated ability to rapidly prototype, experiment, and evaluate new ideas.

Nice To Haves

Experience with large language models, multimodal foundation models, vision-language models, or agentic AI research and development.
Background in 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 in large-scale model training, distributed frameworks such as NeMo or Megatron-LM, GPU computing, or inference technologies such as vLLM or TensorRT-LLM.

Responsibilities

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 datasets and evaluation benchmarks for model training, post-training, and performance analysis.
Prototype model architectures, algorithms, training strategies, and inference techniques, and collaborate with researchers and engineers to scale successful ideas.