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NVIDIA

PhD Researcher Intern, World Foundation Models

NVIDIA is hiring a PhD Researcher Intern, World Foundation Models in Seoul, South Korea. Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • Builds AI ●●●● is how Level rates this role: the job is building AI systems.
  • Tools and skills the posting cites include PyTorch, fine tuning and computer vision.
  • Level classifies it as an internship.
  • Level lists 2,600+ other open roles at NVIDIA.
  • The role was posted in the last few days.

AI in this role

pytorch
fine-tuningcomputer-vision

NVIDIA is seeking PhD research intern to advance world foundation models within NVIDIA Cosmos for Physical AI. Our research explores new model architectures, training objectives, and efficient training and inference for multimodal generation and action-conditioned prediction. We are particularly interested in physical plausibility, spatial and temporal consistency, and the modeling of human appearance, motion, and interactions with objects and environments.


You will work with NVIDIA researchers and engineers to define and pursue a focused research project, develop new methods, and demonstrate measurable improvements through rigorous experimentation. Projects will reflect team priorities and your research strengths. We welcome candidates with relevant research experience in generative modeling, computer vision, multimodal learning, computer graphics, or robotics.


What you'll be doing:

  • Research and develop model architectures, training objectives, and learning algorithms for video generation and world modeling, including diffusion or flow-matching approaches, multimodal conditioning, and spatial and temporal representations.
  • Design controlled experiments and ablation studies, combining quantitative benchmarks with qualitative evaluation to diagnose model behavior, identify failure modes, and validate improvements.
  • Explore training and inference efficiency through architectural changes, distillation, model compression, or other optimization techniques.
  • Develop data curation and evaluation workflows, including model-assisted filtering, annotation, and quality assessment, and measure their impact on model performance.
  • Throughout your project, you will collaborate with research and engineering teams and share findings through reproducible implementations, technical reports, and presentations.

What we need to see:

  • Currently pursuing a PhD in Computer Science, Computer Graphics, Computer Engineering, Robotics, or a closely related field.
  • Research experience in one or more relevant areas, such as generative modeling, computer vision, video understanding or generation, multi-modal learning, 3D vision, or robotics.
  • Strong proficiency in Python and PyTorch, demonstrated through research implementations or substantial projects.
  • Hands-on experience implementing, training or fine-tuning, and evaluating deep learning models.
  • Ability to formulate research questions, design rigorous experiments, analyze results, and communicate findings clearly.
  • Comfortable working in Linux and using standard development practices for ML research, including version control, testing, and experiment reproducibility.

Ways to stand out from the crowd:

  • Publications at venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related conferences, or substantial research projects demonstrating original technical contributions.
  • Background with human appearance or motion modeling, human-object interactions, 3D generative models, 3D/4D representations, or neural rendering.
  • Experience assessing physical plausibility, diagnosing visual and temporal artifacts, or developing evaluations using perceptual metrics, human feedback, or learned evaluators.
  • Experience with distributed training, GPU performance profiling, efficient data loading, or model acceleration.
  • Prior experience with NVIDIA Cosmos is welcome but not required.

Join us to help build the next generation of world models for Physical AI! NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.


Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

How we rate this

PhD Researcher Intern, World Foundation Models at NVIDIA rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningComputer visionPyTorch

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Walk me through a computer vision problem you solved, from raw data to a deployed model.
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, Computer vision, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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