About the role#
NVIDIA is looking for an Applied Research Intern to join the Nemotron post-training team for the Fall 2026 term. This role focuses on the development of large deep learning systems for natural language processing. You will work on the next generation of Nemotron models and contribute to open-source software like NeMo-RL.
What you'll do#
- Develop and prototype new algorithms and models for AI model post-training.
- Contribute to open-source projects, specifically NeMo-RL.
- Build the next version of Nemotron models.
- Publish the results of your internship project.
- Manage large sets of experiments with high attention to detail.
What you'll need#
- Currently pursuing a MS or PhD degree in Computer Science or Electrical Engineering.
- Excellent Python programming skills.
- Strong knowledge of Deep Learning for Natural Language Processing.
- Experience with PyTorch or JAX.
- Ability to work independently.
- A history of contributions to open-source projects is a plus.
Location & details#
- Location: Santa Clara, California.
- Term: Fall 2026.
- Modality: On-site.
- This is a paid, full-time internship position.
About NVIDIA
NVIDIA operates as a computer hardware manufacturer based in Santa Clara, California. Founded in 1993, the company focuses on accelerated computing and graphics technology. It produces hardware for markets including artificial intelligence, gaming, and data centers. The organization employs over 50,000 people and maintains a global presence.
How to get in at NVIDIA
Applying early gives you a distinct advantage at NVIDIA because recruiters review applications as they arrive. Intern Insider sends an instant alert the moment a role matching your target is published, so you can apply among the first before the pile grows. Getting your resume in front of a human early is often the difference between a screening call and a rejection. You can use Intern Insider to surface the recruiters behind NVIDIA roles to reach out directly. Asking a recruiter about the role or a referral materially improves your response rates compared to submitting into a general queue. It is a simple way to make your application stand out in a competitive process.


