About the role
Who you are
- 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
- 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
- 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
What the job involves
- 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
- 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
About Sanctuary AI
Sanctuary is on a mission to create the world’s first human-like intelligence in general-purpose robots that will help us work more safely, efficiently, and sustainably. And in the not-too-distant future, help us explore, settle, and prosper in outer space.
Members of the Sanctuary team founded D-Wave (a pioneer in the quantum computing industry), Kindred (first use of reinforcement learning in a production robot), and the Creative Destruction Lab (pioneered a revolutionary method for the commercialization of science for the betterment of humankind). The team has experience launching market-defining innovations rooted in previously unsolved and deep scientific problems.
About the role
Who you are
- 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
- 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
- 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
What the job involves
- 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
- 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
About Sanctuary AI
Sanctuary is on a mission to create the world’s first human-like intelligence in general-purpose robots that will help us work more safely, efficiently, and sustainably. And in the not-too-distant future, help us explore, settle, and prosper in outer space.
Members of the Sanctuary team founded D-Wave (a pioneer in the quantum computing industry), Kindred (first use of reinforcement learning in a production robot), and the Creative Destruction Lab (pioneered a revolutionary method for the commercialization of science for the betterment of humankind). The team has experience launching market-defining innovations rooted in previously unsolved and deep scientific problems.