Robotics Autonomy Engineer, Locomotion

What You’ll Get To Do

Architect and implement scalable reinforcement learning (RL) pipelines for locomotion and whole-body control on legged and humanoid robots
Design policy architectures, training curricula, and domain randomization strategies that close the sim-to-real gap
Integrate GPU-accelerated, physics-based simulation environments with custom, distributed training workflows
Train locomotion policies from human motion data using imitation learning and motion retargeting, and distill them into compact policies that run on the robot
Create agile, robust, and terrain-aware (perceptive) locomotion behaviors for quadruped and humanoid platforms, and validate them on real hardware
Solve real-world challenges in balance and push recovery, contact-rich dynamics, high degree of freedom (DOF) whole-body coordination, and terrain variability
Automate evaluation across domain-randomized scenarios and maintain simulation infrastructure that enables rapid prototyping, validation, and reproducibility
Work closely with systems engineers, perception experts, and embedded teams, incorporating real-world telemetry and field data to continuously improve generalization
Lead deployment workflows from experiment through lab testing to field robot validation

What You Have

Master’s degree or higher in Robotics, Computer Science, Engineering, or related field (PhD strongly preferred)
Deep expertise in reinforcement learning for continuous control
2+ years of experience developing and deploying locomotion policies on real robotic systems (preferred)
Hands-on experience with legged robot platforms (quadrupeds, bipedal/humanoid systems, or exoskeletons)
Proficiency with simulation tools such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet
Strong command of sim-to-real transfer and a track record of bridging the gap successfully
Solid understanding of contact dynamics, control theory, and kinematics
Strong Python and/or C++ development skills in Linux-based development environments
Familiarity with machine learning frameworks (PyTorch, JAX, TensorFlow)
A passion for building things that move in the real world

The Extras That Set You Apart

3+ years of experience in an industry or startup robotics setting
Experience optimizing and deploying learning based controllers on resource constrained robotic platforms (ONNX Runtime, NVIDIA TensorRT, real time onboard inference)
Publications or open-source contributions in locomotion, reinforcement learning, or control (e.g., CoRL, RSS, ICRA, IROS)
Familiarity with ROS/ROS2 or custom middleware for real-time control
Background in manipulation, loco-manipulation, or whole-body coordination
Experience combining learned policies with model predictive control (MPC) or whole-body controllers
Experience with motion imitation pipelines from motion capture or teleoperation data
Experience debugging sim-to-real issues at scale
Contributions to reinforcement learning libraries or simulation platforms
Prior work on multi-agent learning or terrain-adaptive control systems