Full-Stack Solution Engineer - Sensorized Human

Job description

At NVIDIA, we are closing the "embodiment gap." We don’t just build robots; we build digital and physical nervous systems that allow humans to teach robots. You will lead the development of DexUMI (Dexterous Universal Manipulation Interface), a framework that leverages human-worn hardware and advanced computer vision to transfer complex skills from human hands to robotic actuators. This is a True Full-Stack role in Solutions Architecture Team: you will touch everything from the tactile sensor firmware on a wearable exoskeleton to the cloud-based data pipelines that train our diffusion policies.

What you'll be doing

Hardware-Software Co-Design: Maintain and iterate on the DexUMI wearable exoskeleton. You will bridge the kinematics gap between human hands and diverse robot end-effectors (e.g., XHand, Inspire Hand).

Sensor Fusion & Integration

Integrate high-fidelity tactile sensors and IMUs into wearable interfaces. Ensure low-latency data streaming.

Vision & Perception Pipelines

Implement and optimize the "software adaptation" layer—using tools to segment human operators out of training data and robot embodiments.

Data Engineering for AI

Build robust pipelines to collect, clean, and replay dexterous manipulation data for Imitation Learning and Diffusion Policies.

Optimization

Solve bi-level optimization problems to parameterize exoskeleton designs that maximize human wearability while preserving robot-equivalent fingertip workspaces.

What we need to see

A MS/PhD in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field (or equivalent experience) with at least 1 years of research and engineering experience.

The "Body" (Hardware/Embedded)

Proficiency in C/C++ for embedded systems and ROS2, with hands-on experience in tactile sensing, force-feedback (haptics), and motor control.

Mechatronics & Prototyping

Experience with CAD (SolidWorks/Fusion360) and rapid prototyping, including 3D printing and PCB design.

The "Brain" (Software/AI)

Expertise in Python and deep learning frameworks (PyTorch), with familiarity in computer vision (CV) models.

Advanced AI Techniques (Plus)

Understanding of Imitation Learning or Reinforcement Learning (RL) is a strong plus.

The "Bridge" (Integration)

Experience with Record3D or iPhone-based spatial tracking, enabling integration between perception and physical systems.

Systems & Infrastructure

Experience working in Ubuntu/Linux environments with high-performance data serialization tools such as Protobuf and MQTT.