Physical AI Expert/Advisor

Position: Physical AI Expert/Advisor

Location: Remote

JOB DESCRIPTION:

Role Overview: Provide strategic guidance on embodied AI, robot foundation models, autonomous systems, robotics commercialization, research partnerships, fundraising, and talent acquisition. The advisor serves as a thought partner to executive leadership and helps shape the company's long-term Physical AI roadmap.

Key Responsibilities:

• Advise executive leadership on Physical AI strategy.
• Review technical roadmap and research priorities.
• Evaluate emerging trends in robot foundation models and embodied intelligence.
• Facilitate introductions to investors, strategic partners, customers, and researchers.
• Provide feedback on technical architecture and commercialization strategy.

Qualifications

• Internationally recognized expert in:
  • Robotics
  • Embodied AI
  • Robot Learning
  • Foundation Models
  • Autonomous Systems
  • Physical AI
• 10+ years of leadership in:
  • Research institutions
  • Major AI labs
  • Robotics startups
  • Fortune 500 innovation organizations
• Demonstrated impact through:
  • Publications
  • Patents
  • Startup formation
  • Commercial deployments
  • Industry standards
• PhD or equivalent industry distinction in AI, Robotics, Computer Science, or related field.
• Recognized leadership in Physical AI, embodied intelligence, robotics, or autonomous systems.
• Demonstrated history of influential research, products, or startup creation.
• Strong professional network across academia, industry, and venture capital.

Nice to Have Skills

• Prior advisory experience.
• Experience founding or scaling venture-backed startups.
• Track record of technology transfer from research to commercial deployment.
• Experience with World Foundation Models, Visual-Language-Action (VLA) systems, or humanoid robotics.

Notable Experts:             

• Sergey Levine  - A UC Berkeley professor and co‑founder of Physical Intelligence, known for pioneering deep reinforcement learning and foundational robot‑learning methods that enable robots to acquire complex skills from real‑world data. His research spans end‑to‑end visuomotor learning, inverse RL, and scalable learning‑based control for autonomous robotic systems.
• Chelsea Finn - A Stanford professor and co‑founder of Physical Intelligence whose work focuses on enabling robots to learn generalizable skills through interaction, meta‑learning, and large‑scale robotic data collection. She is recognized for advancing algorithms that let robots rapidly adapt and learn new tasks with minimal supervision.
• Pieter Abbeel  - A UC Berkeley professor and director of the Berkeley Robot Learning Lab, widely regarded for seminal contributions to deep reinforcement learning, imitation learning, and robot skill acquisition. He has founded multiple robotics and AI startups, including Covariant, and helped develop widely used algorithms such as TRPO, GAE, and MAML.
• Deepak Pathak  - A Carnegie Mellon professor and CEO of Skild AI, is known for groundbreaking work in adaptive robot learning, self‑supervised agents, and the development of general‑purpose robotic foundation models. His research demonstrates how robots can learn to act in unpredictable real‑world environments with minimal guidance.
• Marc Raibert - The founder and longtime leader of Boston Dynamics, renowned for creating highly dynamic robots such as BigDog, Atlas, and Spot. As executive director of the Boston Dynamics AI Institute, he continues to drive advances in agile locomotion, manipulation, and next‑generation intelligent machines.