Reinforcement Learning Research Scientist

Key Skills

AI Robotic systems, C++, CUDA kernels, Deep Learning, Docker, Machine learning techniques, Python Programming, PyTorch

Job Description

Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future generations of intelligent machines that will help us all live better lives.

We aim to advance athletic intelligence to new heights by leveraging simulation-based methods like Reinforcement Learning, augmenting them with model information.

Reinforcement Learning Research Scientists will have proven hands-on research or industry experience focusing on one or more of these key areas: Learning-based locomotion, loco-manipulation, or ultra-mobile systems. Having practical hardware experience is essential for this role. If you are passionate about developing technology for robots and using it to advance their capabilities and usefulness, this team will be a great fit for you!

Requirements

• MS / PhD or equivalent industry experience in robotics, computer science, or related fields
• 6+ years of experience in research and development
• Ability to demonstrate technical proficiency in Reinforcement Learning, Control, Robotics, or Imitation Learning
• Experience with sim-to-real for robotic hardware such as legged robots or highly dynamic mobile vehicles.
• Experience in working with perception-based control
• Advanced programming skills in Python or C++
• Expertise with deep learning frameworks such as PyTorch and robotic simulation
• Proven track record in top-tier conferences and journals in Machine Learning, Robotics, Control, or related fields

Bonus

• Knowledge of Model Predictive Control
• Experience in combining model-based and data-driven approaches
• Experience in working with Isaac-Gym, Isaac-Sim, or Orbit
• Experience in working with ROS or ROS2
• Experience with Docker, cloud computing, or similar applications
• Experience with parallel programming (e.g., CUDA)

These attributes are great to have but not required. Candidates who lack these should not be discouraged from applying.