Level

NVIDIA

PhD Research Intern, Autonomous Systems and Physical AI Research - 2027

AI in this role

PhD Research Intern conducting foundational research in autonomous systems, robotics, and physical AI, including foundation models and reinforcement learning.

pytorch
ai-agentsai-researchautonomous-systemsroboticsphysical-aideep-learningreinforcement-learning

We are recruiting outstanding Research Interns to work on autonomous vehicles, robotics, and physical AI!

Intelligent machines that can perceive, reason, and safely interact with people and the physical world are rapidly becoming a reality. Self-driving cars, autonomous delivery and construction vehicles, mobile manipulators, and other robotic systems are moving closer to widespread deployment. However, fundamental research challenges remain before these systems can operate safely, reliably, and autonomously in complex, unfamiliar environments.

For example, how can we:

  • Develop new training paradigms that use perception and interaction to train autonomous vehicle and robot policies in closed loop?

  • Equip autonomous systems with online and offline assurances that meet the requirements of safety-critical applications?

  • Enable vehicles and robots to navigate across new environments, tasks, and embodiments?

  • Build systems that reason under uncertainty and interact safely and naturally with people and other agents?

These are some of the exciting questions being explored by NVIDIA’s Autonomous Systems and Physical AI Research (ASPIRE) group. Our diverse, interdisciplinary team conducts foundational research spanning foundation model design, embodied reasoning, safety, closed-loop training and evaluation, and agentic workflows for Physical AI development. We also investigate related areas including decision-making under uncertainty, deep learning, reinforcement learning, simulation, and the verification and validation of safety-critical AI systems.

Our focus is on fundamental research, and lab members are encouraged to publish their work and open-source their code. NVIDIA is known for its collaborative culture, and our researchers work closely with experts across the company in autonomous vehicles, robotics, perception, simulation, and machine learning. This creates opportunities to influence real-world products while retaining the freedom and bandwidth to conduct groundbreaking, publishable research.

What you’ll be doing:

  • Designing and implementing cutting-edge techniques for autonomous vehicles, robotics, and physical AI.

  • Conducting original research and publishing your results.

  • Collaborating with research colleagues, internal product teams, and external researchers.

  • Transferring technology you’ve developed to relevant product groups.

What we need to see:

  • Currently pursuing a PhD in Robotics, Computer Science, Computer Engineering, or a related field.

  • Relevant research experience in autonomous vehicles, robotics, embodied AI, or autonomous systems.

  • Strong knowledge of the theory and practice of vehicle or robot autonomy—or expertise in a related area, with a strong interest in applying your work to autonomous systems.

  • A track record of research excellence, demonstrated through publications at leading conferences and journals—such as RSS, ICRA, CoRL, IJRR, NeurIPS, ICML, CVPR, TAC, etc.—and through other research artifacts such as open-source software.

  • Exceptional Python programming skills; experience with C++ and parallel programming, such as CUDA, is a plus.

  • Experience with machine learning frameworks such as PyTorch.

  • Strong communication and interpersonal skills are required, along with the ability to thrive in a dynamic, research-focused team.

Are you intellectually curious, collaborative, and motivated to solve complex problems at the intersection of AI and the physical world? If so, you may be a great fit for NVIDIA.

How we rate this

PhD Research Intern, Autonomous Systems and Physical AI Research - 2027 at NVIDIA rates 90 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

AI AgentsAI ResearchAutonomous SystemsRoboticsPhysical AIDeep LearningReinforcement LearningPyTorch

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Tell me about a research question you investigated. What did you find?
  3. Tell me about a project where autonomous systems was part of your work. What did you do?
  4. Tell me about a project where robotics was part of your work. What did you do?
  5. Tell me about a project where physical ai was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI Agents, AI Research, Autonomous Systems, Robotics, and Physical AI. 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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