Overview:
In this role you drive Physical AI for autonomous, lights-out lab operations, leading technical architecture and deployment on robotic hardware. You will bridge foundation AI with real-world devices to enable scalable, autonomous laboratories at global scale. Partnering with gRED and pRED teams, you shape perception, planning, and control pipelines to accelerate drug discovery. You work hands-on to turn simulation-based policies into reliable lab automation. This is a high-impact opportunity to push the frontier of AI-enabled biology at Roche. Compensation / Benefits discretionary annual bonus comprehensive company benefits (per link)
Responsibilities:
Develop 3D vision perception and dynamic motion planning for robotic manipulation and navigation in dynamic workcells Create multi-physics simulation environments to model robot-labware interactions and evaluate AI plans in digital twins Apply sim-to-real tuning and system identification to ensure real hardware follows simulation-trained policies Fine-tune multimodal vision models using synthetic failure data for real-time anomaly detection in the lab Optimize low-latency edge inference and closed-loop control on NVIDIA Jetson hardware Use OpenUSD scene graphs and synchronization services to integrate workcell geometry across diverse hardware APIs
Key requirements:
MS in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or related field with 5+ years of experience, or PhD with 2+ years Strong background in 3D computer vision, kinematics, motion planning (ROS 2, MoveIt 2, cuMotion) Proficiency in PyTorch and C++/Python with robotics simulation frameworks (NVIDIA Isaac Sim/Lab, Newton, MuJoCo, Drake, Gazebo) Experience training or fine-tuning vision/multimodal models (VLMs, Cosmos) and optimizing networks with TensorRT for edge hardware Proficiency in visual servoing, closed-loop controllers, and hardware API integration (gRPC, SiLA 2, vendor SDKs) 3D computer vision ROS 2 MoveIt 2 cuMotion PyTorch NVIDIA Isaac Sim/Lab