About the role#
NVIDIA is seeking an Applied Research Intern to join the Nemotron post-training team for the Fall 2026 term. This role focuses on large deep learning systems for natural language processing and involves working on the next generation of Nemotron models.
What you'll do#
- Develop and prototype new algorithms and models for AI model post-training.
- Contribute to open-source projects such as NeMo-RL.
- Assist in the development of the next version of Nemotron models.
- Publish your internship project results.
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.
- Attention to detail and the ability to manage large sets of experiments.
Location & details#
- Location: Santa Clara, California, United States.
- Term: Fall 2026.
- Modality: On-site.
- Employment type: Full-time.
- Compensation: Paid position with an hourly rate between 20 USD and 71 USD, based on location, education, and experience.
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.
