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From the employer's careers site First seen 18 May 2026 · Still live on 31 Oct 2026

Senior Robotics Research Engineer, Robotics and AI for Drug Discovery

NVIDIA Santa Clara On-site senior full-time

The employer hasn't listed a salary.

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Description

We are now looking for a Senior Robotics Research Engineer to develop fundamental robotics technology and build real-world systems that can bring both automation and autonomy to molecular discovery, manufacturing, testing, and validation.

Using NVIDIA Isaac Sim, Isaac Lab, and Matterix to build digital twins of robots, laboratory environments, and scientific procedures

Developing perception pipelines for object detection, pose estimation, and tracking, leveraging multisensory inputs (e.g., RGB, depth, force/torque, tactile) and foundation models

Translating experimental protocols into executable physical procedures and smooth, collision-free trajectories by using VLMs and developing task and motion planning pipelines

Training robots to solve contact-rich manipulation tasks through a combination of imitation learning, reinforcement learning, and high-performance control

Building real-world systems that perform discovery, manufacturing, testing, and validation procedures with extremely high reliability and efficiency

Collaborating with research scientists in the Seattle Robotics Lab to elevate early-stage research findings and integrate them into a mature automation and autonomy stack

Collaborating with research scientists and engineers in the NVIDIA + Lilly Co-innovation AI Lab with deep expertise across a wide variety of fields (e.g., computational biology and chemistry, generative and agentic AI, bioinformatics, simulation)

Periodically co-authoring publications on technological achievements at high-impact scientific journals and conferences

Skills

Required

Nice to have

  • Bimanual manipulation
  • Mobile manipulation and humanoid loco-manipulation
  • Simulation, sim-to-real, and real-to-sim
  • Multisensory perception (e.g., vision, force/torque, tactile)
  • Task and motion planning
  • Grasp and manipulation planning
  • Imitation learning and reinforcement learning
  • High-performance control
  • Robotics foundation models
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