PhD Autonomy Engineer Intern – Planning & Controls, Reinforcement Learning

Role Overview

• Develop and deploy reinforcement learning (and adjacent policy-learning methods) that make Skydio aircraft plan, navigate, and control themselves more intelligently.
• Train policies that adapt online to cluttered 3D scenes.
• Fuse learned cost shaping / value functions with trajectory optimization for smooth, agile flight.
• Build scalable datasets and training loops with Isaac Lab, domain randomization, and safety filters.
• Learn assistive policies that blend pilot intent, autonomy priors, and uncertainty-aware behaviors.

Requirements

• PhD student in Robotics, Machine Learning, Controls, or related field.
• Strong fundamentals in RL, control theory, and motion planning; comfort with safety/robustness concepts.
• Proficient in Python (PyTorch/JAX/Ray RLlib) and at least one of C++ or CUDA.
• Hands-on experience with robotics simulation (Isaac Lab/MuJoCo/PyBullet) and sim2real techniques.
• Experience training/deploying policies for navigation, manipulation, or locomotion on real robots or autonomous vehicles.

Tech Stack

• Python
• PyTorch
• Ray

Benefits

• Cross-disciplinary mentorship
• Real hardware cadence
• Safety-first learning