Principal Scientist, Physical AI in Vancouver

Your New Role and Team

Sanctuary, a world leader in building AI-based control systems for intelligent robots, is seeking a Principal Scientist to support our next generation of robot learning capability. This role sits at the intersection of deep technical understanding and rigorous validation: you will push the boundary of what robots can learn to do, and you will run the experiments that show which approaches have real merit and are worth scaling into a production capability.

Deep expertise in at least one or more areas of robot learning — RL and imitation learning, simulation and sim2real, data collection, or world and foundation models — and a working knowledge or awareness of the rest. This role begins with reviewing everything we are doing and introducing new approaches: you will scope and run focused, high-leverage projects, and bring in support as your work proves out. Successful projects will drive larger projects and more resources.

Our Success Criteria

• Identify, scope, and lead research initiatives, working with contributors from across the company.
• Design and run experiments that validate early hypotheses, surfacing what works, what does not, and what deserves further investment.
• Demonstrate the merit of new approaches through hands-on experimentation, building the evidence base that demonstrates readiness to scale toward production.
• Ability to create, develop, and enhance cutting-edge robot learning algorithms — RL, imitation learning, world models, foundation models, or approaches we have not tried — and evaluate their performance in practical robotic applications.
• Devise training and data collection pipelines to expedite implementation on physical robots.
• Discover strategies for enhancing current learning processes, considering key performance metrics like sample efficiency, speed, computational resources, and scalability.
• Collaborate within a diverse team to devise, implement, and harden algorithms for production use, and investigate the root causes in existing implementations.
• Translate Machine Learning research and trained models into real-world robotic products, working closely with engineering to validate that promising methods hold up as they move toward our cross-platform software framework.
• Stay current with the latest developments in robot learning — including RL/IL, world models, and foundation models — and their application in robotics.

Your Experience

Qualifications

• Ph.D. in Machine Learning, Computer Science, Applied Mathematics, or equivalent practical background in robot learning.
• 5+ years of hands-on experience implementing and deploying robotic manipulation tasks, both in simulation and on physical robots.
• Experience transitioning Machine Learning research and trained models into real-world production.
• Active involvement integrating Machine Learning models into a robotics platform.
• Strong technical judgment and the ability to form and defend an independent point of view.
• Independently able to evaluate a landscape, propose an approach, complete a proof-of-concept and document and articulate the results to a cross-functional team.
• Comfortable challenging assumptions and driving alignment across technical stakeholders.
• Deep expertise in at least one of:
• Applying various Reinforcement Learning and/or Imitation Learning methods, with focus on robotics in the real world.
• Developing and optimizing large-batch parallel simulations, and proven expertise in sim-to-real transfer.
• Designing and scaling data collection workflows and datasets for robot learning on physical systems.
• Building or fine-tuning world models or foundation models for robotics.

Nice to Have

• Proven expertise in continual learning, employing adaptive model training to improve long-term performance and accuracy.
• Research contributions at venues such as ICRA, IROS, CoRL, or NeurIPS, or open-source work.

Skills

• Development with Python 3.8 or later.
• Working knowledge of PyTorch and/or TensorFlow.
• Familiarity with ROS2.
• Strong understanding of modern robot learning methods and their application.

Traits

• Above all else, a consistently positive attitude and a willingness to do whatever it takes to create robust solutions to complex problems.
• Pragmatic and outcome-oriented, with a bias toward turning advanced research into real-world products.
• Entrepreneurial: comfortable scoping and running a project with a cross-functional team.
• Strong leadership skills in organizing R&D work for projects, with the ability to lead a cross-functional team.
• Curious, adaptable, and willing to go deep into unfamiliar technical areas when needed.
• Patience, persistence, and attention to detail when resolving performance issues.
• Ability to multitask and prioritize in a fast-paced environment.