1.51 Robotics Autonomy Engineer - Locomotion

1. Design and Own RL-Based Locomotion Pipelines

Architect and implement scalable reinforcement learning (RL) pipelines optimized for locomotion and manipulation.

Integrate physics-based simulation environments (Isaac Gym, Isaac Lab, MuJoCo) with custom training workflows.

Develop reward functions, policy architectures, and domain randomization strategies that close the sim-to-real gap.

1. Deploy Locomotion Behaviors on Physical Robots

Create agile, robust locomotion behaviors for quadruped and humanoid platforms, and validate them on real hardware.

Solve real-world challenges in balance, contact-rich dynamics, high-DOF coordination, and terrain variability.

Drive iterative testing across terrain variability and unstructured environments.

1. Build and Scale Simulation Infrastructure

Build scalable training environments using GPU-accelerated simulators.

Automate evaluation across domain-randomized scenarios and domain adaptation protocols.

Maintain simulation infrastructure that enables rapid prototyping, validation, and reproducibility.

1. Collaborate Across the Full Robotics Stack

Work closely with systems engineers, perception experts, and embedded teams to close the loop between learning and execution.

Incorporate real-world telemetry and field data to continuously improve model generalization.

Lead deployment workflows from experiment through lab testing to field robot validation.