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At Reality Labs Research, our goal is to explore, innovate and design novel interfaces and hardware for the next generation of wearable experiences. We are driving research towards a vision of natural, seamless experiences in XR environments that are effective, enjoyable, and functionally indistinguishable from those in the real world. As a Research Scientist Intern, you will work at the intersection of robotic control, mechanics, and machine learning, and you will have the opportunity to work with world-leading collaborators and mentors in these fields. Your primary focus will be on developing novel robotic control policies for dexterous manipulation by combining data-driven approaches with mechanics. Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.
Job Responsibility:
Design and implement novel robotic control policies, leveraging both modern machine learning and physics-based modeling
Conduct research on dexterous manipulation, specifically involving complex object interaction, uncertainty, and deformable bodies
Develop and evaluate algorithms in both high-fidelity simulation and real-world robotic setups
Collaborate with team members to integrate research insights into larger robotics platforms
Prepare and present research findings for internal review and potential publication
Requirements:
Currently has or is in the process of obtaining a PhD in the field of Robotics, Mechanical Engineering, Computer Science, or a related technical field
Demonstrated experience with modern machine learning-based control policies, such as Reinforcement Learning or Imitation Learning
Understanding of classical robotics theory, including kinematics and rigid body dynamics
Experience with solid mechanics and modeling for deformable bodies
Proficiency in programming languages commonly used in robotics and machine learning (e.g., Python, C++)
Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment
Intent to return to degree program after the completion of the internship/co-op
Nice to have:
Experience with Robotics simulation environments (e.g., MuJoCo, Isaac Gym, PyBullet)
Experience with open source solid mechanics simulation tools (e.g. Fenics, Deal-ii, Firedrake)
Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow)
Experience with robotic hardware and real-world experimentation
Experience working and communicating cross functionally in a team environment
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences