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Staff Research Scientist, Dexterous Manipulation

Job type: Full Time · Department: ML Research · Work type: On-Site

Vancouver, British Columbia, Canada

Your New Role and Team

Sanctuary, a world leader in building AI-based control systems for humanoid robots, is seeking a Staff Research Scientist to join our team in engineering and innovating unique robotic manipulation tasks.

As a Staff Research Scientist, your role will involve choosing the most cutting-edge methods, creating training and data collection systems, overseeing the evaluation of these algorithms in simulated environments, and implementing them on our robots in real-world situations. You will also enjoy the exclusive chance to make a meaningful impact by working with novel haptic and proprioceptive sensing techniques, thanks to our in-house robot with dexterous hands.

Success Criteria

  • Create, develop, and enhance cutting-edge Reinforcement Learning (RL) and Imitation Learning (IL) algorithms and evaluate their performance in practical applications

  • Stay current with the latest developments in RL/IL techniques and their application in robotics

  • Identify, communicate, and lead research initiatives that show promise to the wider ML team

  • Discover strategies for enhancing current RL/IL learning processes, considering key performance metrics like sample efficiency, speed, computational resources, and scalability

  • Devise RL/IL training and data collection pipelines to expedite implementation on physical robots

  • Collaborate within a diverse team to devise innovative algorithms and investigate the root causes of errors in existing implementations

Your Experience

Qualifications

  • Ph.D. in Machine Learning, Computer Science, Applied Mathematics, or equivalent practical background in Reinforcement Learning and/or Imitation Learning

  • 5+ years of hands-on experience implementing and deploying robotic manipulation tasks, both in simulation and on physical robots

  • 5+ years of practical experience applying various Reinforcement Learning and/or Imitation Learning methods, with focus on robotics in the real world

  • 4+ years experience in developing and optimizing large-batch parallel simulations for Reinforcement Learning

  • Proven expertise in continual learning, employing adaptive model training to improve long-term performance and accuracy

  • Proven expertise in sim-to-real transfer

  • Experience in transitioning Machine Learning research and trained models into real-world production

  • Active involvement in integrating Machine Learning models into a robotics platform

  • A track record of publishing research in esteemed AI conferences such as ICRA, IROS and CORL

Skills

  • Development with Python 3.8 or later

  • Working knowledge of PyTorch and/or TensorFlow

  • Familiarity with ROS2

  • Expertise in use of Reinforcement Learning principles and their application

  • Experience with Atlassian tools; Jira, Confluence, or equivalent i.e. GitLab

Traits

  • Above all else, a consistently positive attitude and a willingness to do whatever it takes to create robust solutions to complex problems

  • Strong leadership skills in organizing R&D work for ML projects

  • Eager to take on new challenges with tenacity and positivity

  • Patience, persistence, and attention to detail when resolving performance issues

  • Enthusiasm for bringing human-like intelligence to machines

  • Ability to drive development of new functionalities from concept to production

  • Ability to multitask and prioritize in a fast paced environment

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