Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI
CUDA @ 3
Communication @ 3
Deep Learning @ 3
JAX @ 3
LLM
PyTorch @ 3
Python @ 3
RAG @ 3
Reinforcement Learning @ 3
Robotics
TensorFlow @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
By submitting your resume, you acknowledge that your Ph.D. Research Large Language Models internship application will be processed in accordance with NVIDIA's Applicant Privacy Policy and Terms of Service. Resumes will be reviewed on an ongoing basis, and a recruiter may reach out if your experience fits one of NVIDIA's internship opportunities.
NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Its work in AI and digital twins is transforming industries and impacting areas including gaming, robotics, self-driving cars, healthcare, climate change, and virtual worlds.
This internship offers hands-on experience with one of NVIDIA's leading large language model teams. NVIDIA is seeking strategic, ambitious, hardworking, collaborative, and creative individuals who are passionate about solving challenging problems.
Responsibilities
- Research and develop novel methods for advancing the capabilities of large language and multimodal models.
- Collaborate with team members, other teams, and external researchers.
- Transfer research to product groups to enable new products or product categories.
- Deliver results through prototypes, patents, products, and/or original research publications.
Requirements
- Actively enrolled in a university Ph.D. program in Computer Science, Electrical Engineering, or a related field for the full duration of the internship.
- Clearly indicate the anticipated graduation month and year on your resume or CV.
- Depending on the internship, prior experience or knowledge may include:
- Python
- C++
- CUDA
- Deep learning frameworks such as PyTorch, TensorFlow, and JAX
- Strong research background with publications at top conferences.
- Excellent communication and collaboration skills.
- Experience with large-scale model training is a plus.
Potential internships require research experience in at least one of the following areas:
- Large language models and foundation models: Transformer architectures, knowledge distillation and data synthesis, and long-context methods.
- Model efficiency and optimization: Model compression and pruning, quantization, inference optimization and acceleration, parameter-efficient fine-tuning, and Neural Architecture Search (NAS).
- Training and alignment: Large-scale model training, instruction tuning, reinforcement learning, advanced reasoning and test-time inference, and few-shot and zero-shot learning.
- Synthetic data generation.
- Multimodal and vision-language models.
- Retrieval-Augmented Generation (RAG).
Benefits
Interns are eligible for NVIDIA's intern benefits. Applications are accepted on an ongoing basis. NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.