Vice President, Robot Learning

Location: Palo Alto, United States Vice President, Robot Learning Confidential | Early-Stage Robotics Startup | San Francisco Bay Area, CA About the Opportunity We're partnering with an ambitious early-stage robotics company building the next generation of humanoid telepresence systems for industrial work. The company's 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. These systems are deployed in complex environments including energy infrastructure, industrial facilities, manufacturing, and other hazardous settings. A core part of the long-term vision is transforming teleoperation data into increasingly autonomous robotic capabilities. Rather than treating teleoperation as a temporary solution, the company views it as the foundation for building high-quality robot learning systems based on real-world interaction, force feedback, and task execution. This is an opportunity to define the robot learning strategy at a company where machine learning is central to the product roadmap. The Role We're seeking a Vice President of Robot Learning to lead the company's robot learning organization and own the roadmap from teleoperation data collection through to learned policies and increasing levels of autonomy. This is a highly technical leadership role for someone who enjoys building from first principles, working closely with hardware and software teams, and translating cutting-edge robotics research into production systems. You'll have the opportunity to shape both the technical direction and the team as the company scales. What You'll Do 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. What We're Looking For Required 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 Experience from leading robotics organizations or research groups such as: OpenAI Robotics Google DeepMind Robotics Physical Intelligence Toyota Research Institute Figure AI Agility Robotics Covariant Skild AI Sanctuary AI Dexterity Intrinsic Boston Dynamics Other leading manipulation or humanoid robotics companies Particularly valuable experience includes: 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 Who You Are Builder-first with a passion for deploying robotics in the real world. Comfortable operating in an early-stage, fast-moving environment. Excited by ambiguous technical problems with significant ownership. Able to balance long-term research vision with near-term product execution. Motivated by building a team, not just contributing individually. Collaborative, hands-on, and deeply technical. Why Join Define the robot learning strategy from an early stage. Build systems that operate in challenging, real-world industrial environments. Work across hardware, controls, perception, and AI to solve end-to-end robotics problems. Help shape the future of intelligent humanoid robotics. Significant technical ownership with the opportunity to build and lead a high-impact organization. Location: San Francisco Bay Area, California (onsite) Employment Type: Full-time