Autonomy Engineer Intern, Reinforcement Learning, 2027 (HQ)

📋 Description

• Develop and deploy reinforcement learning (and adjacent policy-learning methods) that make Skydio
• Train policies that adapt online to cluttered 3D scenes, complementing the stack for robust
• Fuse learned cost shaping / value functions with trajectory optimization for smooth, agile flight
• Build scalable datasets and training loops with Isaac Lab, domain randomization, residual learning
• Learn assistive policies that blend pilot intent, autonomy priors, and uncertainty-aware behaviors
• Explore decentralized coordination for fleet and multi-agent coverage, pursuit, and collaborative

🎯 Requirements

• PhD student in Robotics, Machine Learning, Controls, or related field.
• Strong fundamentals in reinforcement learning, control theory, and motion planning; comfort with
• 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

🎁 Benefits

• Hands-on experience with production-ready aircraft and edge compute.
• Mentorship from cross-disciplinary teams in perception, mapping, controls, and product.
• Opportunity to collaborate with perception, mapping, controls, and product teams to close the loop
• Eligibility for Skydio benefit plans and various incentives and stipends for full-time employees.