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 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 #J-18808-Ljbffr