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Overview
Mid-Level

Research Scientist, Atlas Behavior Learning

Confirmed live in the last 24 hours

Boston Dynamics

Boston Dynamics

Waltham Office (POST)
On-site

Job Description

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 Research Scientist on the Atlas Behavior Learning 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) and behavioral cloning (BC) as a key technology for achieving dexterous and robust whole-body manipulation that can be deployed in real-world environments. 

In this role, you will be responsible for:

  • Design, train, and deploy state of the art robot learning algorithms to tackle mobile &cz bimanual manipulation tasks

  • Contribute to foundation models shaping the future of humanoid robotics

  • Train control policies using RL to solve dexterous manipulation tasks

  • Distil many different policies using BC and RL with pixel observations

  • Deploying and debugging learned policies on Atlas


We are looking for:

  • MS with 3 years of industry experience or PhD in Computer Science, Machine Learning, Robotics, or a related field

  • Extensive Experience training and deploying RL policies for complex behaviors on real robots or simulated characters

  • Excitement for integrating, running, and evaluating their learned policies on the robot

  • Has in-depth knowledge about domain randomization to bridge the sim-2-real gap

  • Strong foundation in Python and modern ML frameworks  (e.g., PyTorch and Jax)

  • Experience in algorithm design, debugging, and performance optimization

The ideal candidate has: 

  • A PhD or equivalent research experience in reinforcement learning or robotic manipulation

  • Publications at top tier venues including RSS, CoRL, Science Robotics, ICLR, NeuRIPS

  • Familiarity with behavior cloning and has trained a diffusion policy from images

  • Prior experience with student-teacher training workflows 

Why join us? 

  • Direct access to cutting-edge robots and the infrastructure to run large-scale experiments

  • A collaborative, mission-driven team where your ideas have real impact

  • The chance to help define what’s possible in real-world robotics

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