Develop and train whole-body loco-manipulation policies for humanoid robots.

Deploy trained policies to physical humanoids and integrate them into our production software stack.

Advance our motion retargeting pipeline, transforming human motion into physically plausible, robot-executable behaviors that interact with diverse environments and objects.

Reduce the sim-to-real gap by improving simulation fidelity and closing the real-to-sim loop, using real-world robot data to calibrate and refine our simulators.

Build automated evaluation and validation systems that make it faster and more reliable to move policies from simulation onto physical robots.

Improve the performance and scalability of our robot-learning infrastructure, enabling faster experimentation and policy iteration.

Work closely with researchers and engineers across perception, learning, simulation, and hardware to turn new ideas into deployed robot capabilities.

BS, MS, or PhD in Robotics, Computer Science, Machine Learning, Engineering, or a related field, or equivalent experience.

Experience with reinforcement learning, imitation learning, generative models, or other learning-based approaches for robotics.

Strong understanding of robotics fundamentals such as kinematics, dynamics, control, and physical interaction.

Experience developing and evaluating robotic systems in simulation and/or on physical hardware.

Ability to move comfortably between research experimentation and production-quality engineering.

Hands-on experience with humanoid robots.

Experience with whole-body loco-manipulation.

Experience with GPU-accelerated simulation frameworks such as NVIDIA Isaac Sim, Isaac Lab, and/or Newton.

Experience with motion retargeting.

Experience taking learned robot behaviors from simulation to real hardware.

Experience building scalable RL training, evaluation, and/or automated robot-testing infrastructure.