Our Success Criteria
- Design, implement, and improve state-of-the-art Reinforcement Learning (RL) and Imitation Learning (IL) algorithms and test them in real-world settings
- Keep up to date with state-of-the-art RL/IL methodologies and robotics
- Identify, communicate, and drive promising research directions to the team
- Find ways of improving existing implementations of RL/IL pipelines with regards to standard metrics such as sample efficiency, speed, computational resource usage, and scalability
- Design RL/IL training and data-collection pipelines to facilitate fast deployment on physical robots
- Work with a multidisciplinary team to develop novel algorithms and investigate sources of errors with existing implementations
- Pursuing MS or Ph.D. in Machine Learning, Computer Science, Applied Math, or related field
- Experience implementing a variety of RL and IL methods with a focus in a specialization such as computer vision or robotics
- Hands-on experience integrating ML models onto a robotics platform
- Experience implementing and deploying (dexterous) robotic manipulation tasks in simulation and on physical robots
- Experience taking ML R&D and trained models into production
- Experience with computer vision systems
- Experience in simulation-to-reality transfer learning
- Development with Python 3.6 or later
- Working knowledge of PyTorch and/or JAX
- Familiarity with ROS2
- Extensive knowledge of RL/IL principles and use
- Experience with Atlassian tools; Jira, Confluence, or equivalent e.g. GitLab
- Above all else, a consistently positive attitude and a willingness to do whatever it takes to create robust solutions to complex problems
- Optimistic listening and conflict resolution capabilities
- Demonstrated ability to influence others without authority
- Eager to take on new challenges with tenacity and positivity
- Patience, persistence, and attention to detail when resolving performance issues
- Obsession with bringing human-like intelligence to machines
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