Deep Learning Scientist Intern

Join NVIDIA's team and help build the next generation of large language models, multimodal foundation models, and agentic AI systems.

As a Deep Learning Scientist Intern, you will work on research, design, and development of large language models, multimodal LLMs, and agentic AI systems.

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 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.

Requirements:

• 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.

Nice to Have:

• 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.

Benefits:

• Intern benefits
• Expected hourly rate: 20 USD – 94 USD