Are you passionate about using machine learning to drive robot behavior? Curious what you’d be able to accomplish with total access to Boston Dynamics robots?
As a Reinforcement Learning Research Scientist on the Atlas team, you will join a world-class team of engineers and scientists focused on creating groundbreaking mobile manipulation behaviors for humanoids. We are investing in reinforcement learning (RL) as a key technology for achieving robust whole-body manipulation that can be deployed in real-world environments. In this role, you will be responsible for architecting, training, and deploying learned models to expand the capabilities of Atlas to solve challenging manipulation tasks. Come help us redefine the state of the art in humanoid RL!
How you will make an impact:
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Creatively apply machine learning to solve real-world bimanual manipulation problems
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Write high-quality (documented, tested, maintainable) Python and C++ code
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Lead design reviews and collaborate closely with members of the Atlas software team
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Deploy and debug your code on Atlas
We are looking for:
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MS in Computer Science, Machine Learning, Robotics, or a related field
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Prior experience training and deploying RL policies for complex behaviors in robots or simulated characters
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Experience with common ML tools, architectures, and workflows
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Strong analytical and debug skills
Nice to have:
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PhD in Computer Science, Machine Learning, Robotics, or a related field
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Experience working with and contributing to large codebases
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Experience working hands on with robot hardware
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What We Do
Team, Inc. is a leading industrial services company offering an array of specialized services related to the construction, maintenance, and monitoring of pressurized piping and associated systems. We serve customers in the refining, petrochemical, power, pipeline, and other heavy industrial industries with a level of service that goes above and beyond. With more than 220 U.S. and international locations, we are rapidly growing our global footprint across a wide range of industries - with service locations in five continents.
