The Atlas team at Boston Dynamics is pushing the limits of robotic mobility and manipulation. As a Teleoperation Research Scientist, you will design and implement cutting-edge teleoperation and human-robot interaction systems that enable humanoid robots to execute complex, coordinated tasks in real-world environments. You will work closely with cross-functional teams in controls, perception, machine learning, and hardware to build intuitive and scalable teleoperation interfaces that bridge the gap between human demonstration and autonomous robot behavior, facilitate large-scale data collection for foundation models, and advance research in dexterous manipulation, whole-body mobile teleoperation, and human-robot interfaces. This role combines deep technical research with hands-on experimentation on one of the most advanced humanoid platforms in the world.
How you will make an impact:
Implement and evaluate novel teleoperation interfaces (e.g., VR, motion capture, wearable devices, or mixed reality systems) to collect high-quality manipulation and locomotion demonstrations for learning-based control.
Integrate and optimize hardware and software components for low-latency, high-bandwidth communication and control.
Analyze demonstration data to improve data efficiency, operator ergonomics, and task performance.
Write high-quality (documented, tested, maintainable) C++ and Python code.
Support integration of teleoperation and learned policies into end-to-end proof-of-concept demonstrations.
What we are looking for:
4+ years of industry experience or PhD in Robotics, Computer Science, Human-Computer Interaction, or a related field.
Experience with real-time control, sensing, and communication systems in robotics.
Proficiency in Python and/or C++
Strong analytical and debugging skills.
Strong communication and collaboration skills
Nice to have:
PhD in Robotics, Computer Science, Human-Computer Interaction, or a related field.
Prior experience with VR/AR-based teleoperation systems, haptics, and force-feedback systems.
Experience with robot hardware and real-world robot deployments
Experience with user studies and quantitative evaluation of human-robot systems.
Familiarity with large-scale data collection for learning-based control or foundation model training.