Senior Research Scientist - Autonomous Vehicles

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Research Scientist role focused on AI for autonomous vehicles, involving designing and implementing techniques, publishing research, and collaborating with product teams for deployment. The role emphasizes agent behavior, foundation models, closed-loop training, and AI safety within the robotics domain.

What you'd actually do

  1. Designing and implementing cutting-edge techniques in the field of vehicle autonomy.
  2. Publish and present your original research, speak at conferences and events.
  3. Collaborate with other research team members, a diverse set of internal product teams, and external researchers.
  4. Have a broader impact through the transfer and/or open-source of the technology you've developed to relevant product groups.
  5. Collaborate closely with product teams to transfer research results into high-end, production-level systems.

Skills

Required

  • Ph.D. in Computer Science/Engineering, Electrical Engineering, or a related field (or equivalent experience).
  • 6+ years of relevant research experience in the field of vehicle / robot autonomy.
  • Strong knowledge of theory and practice of vehicle / robot autonomy, or a related area with a strong interest in connecting your work to autonomous vehicles.
  • Excellent collaboration and interpersonal skills
  • Strong communication and interpersonal skills are required along with the ability to work in a dynamic, research-focused team.

Nice to have

  • Python
  • C++
  • CUDA
  • PyTorch
  • Tensorflow

What the JD emphasized

  • track record of research excellence with your work published in top conferences and journals such as RSS, ICRA, IJRR, NeurIPS, ICML, CVPR, TAC, etc, and other research artifacts such as software projects
  • Exceptional programming skills in Python; C++ and parallel programming (e.g., CUDA).
  • Solid understanding of common machine learning frameworks such as PyTorch and Tensorflow.

Other signals

  • AI-powered autonomous vehicles
  • agent behavior models
  • foundation models (VLMs, reasoning models)
  • closed-loop training
  • AI safety