Level

NVIDIA

Robotics Simulation and Evaluation PhD Intern - Summer 2027

NVIDIA is hiring a Robotics Simulation and Evaluation PhD Intern - Summer 2027 in Zurich, Switzerland. Level rates it ; you can apply on Level.

AI in this role

PhD robotics simulation and evaluation intern to develop agentic robot policies and sim-to-real transfer methods using GPU simulation.

pytorchjaxpython
fine-tuningroboticsreinforcement-learningrobot-learningsimulationmachine-learning

Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in an inclusive, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world.


We are looking for dedicated interns to join our robotics team. You will develop novel approaches to agentic robot policies, world action models, robot policy training and evaluation, and sim-to-real transfer, working alongside the researchers and engineers who build NVIDIA's robotics and simulation platforms. Our internships are an excellent opportunity to grow as a researcher and gain hands-on experience with industry-leading robotics teams. We are looking for curious, ambitious, and collaborative individuals who take ownership of open-ended problems and are excited to help us tackle challenges no one else can solve.


What you'll be doing

  • Work closely with experts in robotics and machine learning to scope a ch project that is both scientifically novel and relevant to NVIDIA's robotics roadmap.
  • Design, implement, and evaluate new robot learning methods using GPU-accelerated simulation and, where appropriate, real robot hardware.
  • Run rigorous experiments: build strong baselines, design evaluation protocols, ablate your ideas, and communicate clearly.
  • Write clean, well-documented research code that other team members can build on.
  • Collaborate with researchers and internal product teams to transfer your research into NVIDIA products and platforms.
  • Deliver results as prototypes, patents, product contributions, and publications at top-tier venues.

What we need to see

  • Currently enrolled in a Ph.D. program in Computer Science, Electrical Engineering, Robotics, or a related field.
  • Research experience in one or more of the following: agentic policies, world action models, robot policy training and evaluation, or sim-to-real transfer, demonstrated through publications, preprints, a thesis, or open-source projects.
  • Strong Python skills and hands-on development experience with a modern deep learning framework (e.g., PyTorch, JAX).
  • Strong written and verbal communication skills, including presenting research results to both experts and non-experts.

Ways to stand out from the crowd

  • First-author publications at top robotics and machine learning venues (e.g., CoRL, RSS, ICRA, IROS, NeurIPS, ICML, ICLR, CVPR).
  • Deep expertise in robot learning: reinforcement learning, imitation learning, or training and fine-tuning large vision-language-action (VLA) or world models for robotics.
  • Deep expertise in GPU-accelerated physics simulation (e.g., PhysX, Isaac Gym / Isaac Lab, Newton, MJX), including building simulation environments, assets, or domain randomization pipelines.
  • Experience deploying learned policies on real robot hardware (manipulators, humanoids, or mobile robots) and closing the sim-to-real gap.
  • Experience with large-scale, multi-GPU training and data pipelines with contributions to open-source robotics or simulation projects.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!

How we rate this

Robotics Simulation and Evaluation PhD Intern - Summer 2027 at NVIDIA rates 95 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. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine-tuningRoboticsReinforcement learningRobot LearningSimulationMachine learningPyTorchJax

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. Tell me about a project where robotics was part of your work. What did you do?
  3. Tell me about a project where reinforcement learning was part of your work. What did you do?
  4. Tell me about a project where robot learning was part of your work. What did you do?
  5. Tell me about a project where simulation was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, Robotics, Reinforcement learning, Robot Learning, and Simulation. 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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