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

NVIDIA is hiring a Deep Learning Scientist Intern, LLM, Multimodal and Agentic AI - Summer 2027 in Santa Clara, United States. It pays $20-$94/hr and Level rates it Builds AI ●●●●; you can [apply on NVIDIA](https://jobsbylevel.com/go/729c4092-04c2-4bbd-8971-bc3b9561610d?utm_medium=ai-agent).

AI Level 4, AI centrality 100 out of 100. US, CA, Santa Clara.

## Details

- Company: [NVIDIA](https://jobsbylevel.com/companies/nvidia)
- AI level: AI Level 4 (score 100 out of 100)
- Location: US, CA, Santa Clara
- Salary: $20-$94/hr
- Posted: November 4, 2026
- Apply: https://jobsbylevel.com/go/729c4092-04c2-4bbd-8971-bc3b9561610d?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=deep-learning-scientist-intern-llm-multimodal-and-agentic-ai-summer-2027-at-nvidia-d68a7a

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/deep-learning-scientist-intern-llm-multimodal-and-agentic-ai-summer-2027-at-nvidia-d68a7a

## Description

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” We're looking to grow our company and build our teams with the smartest people in the world. Would you like to join us at the forefront of technological advancement? Join our team and help build the next generation of large language models, multimodal foundation models, and agentic AI systems. Intelligent machines powered by AI that can understand, reason, plan, interact with people, and take actions are no longer science fiction. Today, LLMs and multimodal models can process and generate text, vision, speech, audio, and video, while increasingly using tools and interacting with complex environments to solve real-world tasks. This is truly an extraordinary time. The era of AI has begun. What you'll be doing: As a Deep Learning Scientist Intern, you will work on research, design, and development of large language models, multimodal LLMs, and agentic AI systems. You will use machine learning and deep learning techniques across language, vision, speech, audio, and video to develop and improve next-generation foundation models. 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

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