Machine Learning Research Intern

Who you are

• 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
• 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

What the job involves

• Reporting to the RL Lead, you will have the opportunity to tackle a variety of challenges related to the perception, planning, and motion systems for humanoid general-purpose robots
• 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