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NVIDIA

Designs the chips and software that power AI

Deep Learning Scientist Intern - LLM, Multimodal and Agentic AI

AI Research +1Posted on Nov 4
Internship · Summer 2027$20 - $94 /hrIn-person

About the Job

Requirements

  • Pursuing an MS or PhD degree in Computer Science, Electrical Engineering, Artificial Intelligence, Applied Mathematics, or a related field.
  • Have strong knowledge of machine learning and deep learning techniques, algorithms, and modern foundation model architectures, including Transformers.
  • Have 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.
  • Have excellent programming skills in Python and experience with deep learning frameworks such as PyTorch, NeMo, Megatron-LM, TensorFlow, or related frameworks.
  • Have experience developing, training, fine-tuning, or evaluating deep learning models, including familiarity with data processing, dataset development, experiment tracking, and model evaluation workflows.
  • Have strong problem-solving skills and the ability to rapidly prototype, experiment, and evaluate new ideas.

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.

Desired Qualifications

  • Have experience with large language models, multimodal foundation models, vision-language models, or agentic AI research and development.
  • Have a background with post-training techniques such as instruction tuning, preference optimization, RLHF, RLVR, GRPO, or related reinforcement learning methods.
  • Have experience with multimodal reasoning across text, image, video, speech, or audio, including conversational or audio-language models.
  • Have experience with reasoning, planning, tool use, function calling, or multi-step agentic systems.
  • Have a 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.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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