Quick overview
Job description
What You Will Get To Do
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Develop and train whole-body loco-manipulation policies for humanoid robots.
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Deploy trained policies to physical humanoids and integrate them into our production software stack.
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Advance our motion retargeting pipeline, transforming human motion into physically plausible, robot-executable behaviors that interact with diverse environments and objects.
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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.
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Build automated evaluation and validation systems that make it faster and more reliable to move policies from simulation onto physical robots.
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Improve the performance and scalability of our robot-learning infrastructure, enabling faster experimentation and policy iteration.
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Work closely with researchers and engineers across perception, learning, simulation, and hardware to turn new ideas into deployed robot capabilities.
What You Bring
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BS, MS, or PhD in Robotics, Computer Science, Machine Learning, Engineering, or a related field, or equivalent experience.
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Experience with reinforcement learning, imitation learning, generative models, or other learning-based approaches for robotics.
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Strong understanding of robotics fundamentals such as kinematics, dynamics, control, and physical interaction.
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Experience developing and evaluating robotic systems in simulation and/or on physical hardware.
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Ability to move comfortably between research experimentation and production-quality engineering.
What Will Set You Apart
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Hands-on experience with humanoid robots.
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Experience with whole-body loco-manipulation.
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Experience with GPU-accelerated simulation frameworks such as NVIDIA Isaac Sim, Isaac Lab, and/or Newton.
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Experience with motion retargeting.
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Experience taking learned robot behaviors from simulation to real hardware.
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Experience building scalable RL training, evaluation, and/or automated robot-testing infrastructure.
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