Vice President, Robot Learning

About the Company

An ambitious early-stage robotics company building the next generation of humanoid telepresence systems for industrial work. The mission is to make dangerous, highly skilled physical work safer and more accessible by enabling expert operators to remotely control humanoid robots with real-time visual, auditory, and haptic feedback.

Responsibilities

• Own the robot learning strategy and technical roadmap
• Build the data pipeline that transforms teleoperation sessions into high-quality training data
• Develop learning systems for contact-rich robotic manipulation using real-world demonstrations
• Design and deploy imitation learning, reinforcement learning, and policy optimization approaches for physical robots
• Work closely with robotics, controls, perception, and hardware engineers to build an integrated robotic system
• Lead the evolution from teleoperated workflows toward progressively autonomous capabilities
• Build, mentor, and grow a world-class robot learning organization
• Establish technical direction, research priorities, and engineering best practices
• Drive experimentation, deployment, and continuous improvement using real-world operational data

Requirements

• PhD or equivalent expertise in Robotics, Machine Learning, Computer Science, or a related field
• Significant experience building robot learning systems for real-world robotic manipulation
• Deep expertise in imitation learning, reinforcement learning, policy learning, or related techniques
• Experience working with physical robots rather than purely simulated environments
• Strong understanding of contact-rich manipulation, force feedback, and real-world robotic control
• Track record of shipping robotics systems from research into production
• Experience leading highly technical teams or serving as the technical leader for a major robotics initiative

Preferred Qualifications

• Experience from leading robotics organizations or research groups (e.g., OpenAI, DeepMind, Figure AI, Boston Dynamics)
• Experience learning from teleoperation or human demonstrations
• Building commercial robot learning data pipelines
• Manipulation in contact-rich environments
• Haptic interfaces or force-controlled robotics
• Multi-modal learning using vision, force, and proprioception
• Scaling robot learning infrastructure across fleets