Drimva

NVIDIA

Senior Robotics Research Engineer, Robotics and AI for Drug Discovery

$184K–$356K

US, CA, Santa Clara · Senior · Full-time · Posted Apr 8, 2026

Experience
At least 3 years after completing the PhD; 5+ years preferred.
Education
PhD in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field (or equivalent experience).

About the job

Develops robotics and AI technologies and reliable automated systems for laboratory workflows in drug discovery, manufacturing, testing, and validation.

What you'll do

  • Build digital twins and physics simulations of robots, laboratories, and scientific procedures; develop perception, planning, and control systems.
  • Train robots for manipulation tasks and integrate reliable robotic systems into biological experiment workflows.
  • Collaborate with research teams to mature robotics research into automation systems and co-author scientific publications.

What they ask for

  • PhD in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field (or equivalent experience).
  • At least 3 years of research and engineering experience after completing the PhD.
  • Deep knowledge of robotics and AI, with strong expertise in real-world robotics applications.
  • Strong programming skills in Python; fluency with PyTorch and JAX; significant experience with ROS2 and physics simulation frameworks such as Isaac Sim, Isaac Lab, and MuJoCo.

Nice to have

  • 5+ years of post-PhD research and engineering experience.
  • Direct experience in scientific and laboratory automation; familiarity with C++, CUDA, and Warp is also a plus.

Pay

184,000 USD - 287,500 USD base salary (Level 4); 224,000 USD - 356,500 USD base salary (Level 5)

Tools

NVIDIA Isaac Sim, Isaac Lab, Matterix, NVIDIA Newton, Python, PyTorch, JAX, ROS2

AI focus

Applies AI and robotics to drug discovery and laboratory automation, including perception and foundation models, VLM-based procedure translation, task and motion planning, imitation and reinforcement learning, and robotics foundation models.

Model-written summary · the original posting is authoritative

First seen Oct 8, 2026 · Last verified Nov 6, 2026 (UTC)