Senior Robotics Research Engineer, Robotics and AI for Drug Discovery

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Robotics Research Engineer focused on building physical AI for drug discovery labs, involving robotics simulation, perception, task and motion planning, and training robots for manipulation tasks using imitation and reinforcement learning.

What you'd actually do

  1. Using NVIDIA Isaac Sim, Isaac Lab, and Matterix to build digital twins of robots, laboratory environments, and scientific procedures
  2. Using NVIDIA Newton to simulate the physics of robots, articulated rigid bodies, deformable objects, granular media, and fluids
  3. Developing perception pipelines for object detection, pose estimation, and tracking, leveraging multisensory inputs (e.g., RGB, depth, force/torque, tactile) and foundation models
  4. Translate experimental protocols into executable physical procedures and smooth, collision-free trajectories by using VLMs and developing task and motion planning pipelines
  5. Training robots to solve contact-rich manipulation tasks through a combination of imitation learning, reinforcement learning, and high-performance control

Skills

Required

  • PhD in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or related field (or equivalent experience)
  • 3+ years of research and engineering experience post-PhD
  • Deep knowledge of robotics and AI theory and practice
  • Strong expertise in real-world robotics applications
  • Exceptional Python programming skills
  • Fluency in modern deep learning frameworks (PyTorch, JAX)
  • Experience training deep learning models on GPU clusters
  • Significant experience with robotics frameworks (ROS2)
  • Significant experience with physics simulation frameworks (Isaac Sim, Isaac Lab, MuJoCo)
  • Experience with simulation and real-world robotics complexities
  • Experience debugging physics simulators and renderers
  • Experience selecting, setting up, maintaining, and enhancing robotics hardware
  • Experience debugging communication systems
  • Experience designing robust workflows for model training and evaluation
  • Willingness to experiment with robotics and AI technology (foundation models, world models, agentic AI)
  • Ability to quickly learn biology, chemistry, laboratory tasks, hardware, and automation standards

Nice to have

  • C++
  • CUDA
  • Warp
  • Direct experience in scientific and laboratory automation
  • Experience with humanoid loco-manipulation
  • Experience with multisensory perception
  • Experience with task and motion planning
  • Experience with grasp and manipulation planning
  • Experience with imitation learning and reinforcement learning
  • Experience with high-performance control
  • Experience with robotics foundation models

What the JD emphasized

  • A 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; 5+ years is preferred.
  • Deep knowledge of both the theory and practice of robotics and AI, with particularly strong expertise in real-world robotics applications.
  • Exceptional communication, collaboration, and interpersonal skills, with significant experience working on teams as both a leader and a contributor.
  • Exceptional programming skills in Python; in addition, familiarity with C++, CUDA, and Warp is a plus.
  • A track record of writing clean, high-quality code in collaboration with team members, using standard methodologies in software engineering (e.g., unit tests, version control, CI/CD).
  • Fluency in modern deep learning frameworks such as PyTorch and JAX, as well as training deep learning models on GPU clusters.
  • Significant experience with robotics frameworks such as ROS2 and physics simulation frameworks such as Isaac Sim, Isaac Lab, and MuJoCo.
  • Deep comfort in working through the complexities of simulation and real-world robotics, including debugging physics simulators and renderers under rapid development; selecting, setting up, maintaining, and enhancing complex robotics hardware; debugging communication systems (latency, bandwidth, race conditions); and designing robust workflows for model training and evaluation.
  • A willingness to embrace and experiment with rapidly-developing robotics and AI technology, such as robotics foundation models, world models, and agentic AI as well as rapidly get up-to-speed on biology and chemistry fundamentals, laboratory tasks, laboratory hardware, laboratory automation standards, and safety and regulatory constraints.

Other signals

  • building physical AI for wet labs
  • develop fundamental robotics technology
  • bring both automation and autonomy to molecular discovery