Autonomy Engineer Intern

About the Role

Skydio builds the world’s most advanced autonomous drones used across inspection, public safety, defense, cinematography, and more. Your research will shape how pilots and operators complete real missions in complex environments. Develop and deploy reinforcement learning (and adjacent policy-learning methods) that make Skydio aircraft plan, navigate, and control themselves more intelligently, safely, reliably, and efficiently across our ecosystem: handheld apps, ground control, cloud autonomy services, and fleet workflows.

Key Responsibilities

• Navigation & avoidance in the wild: Train policies that adapt online to cluttered 3D scenes (forests, bridges, urban canyons), complementing our stack for robust obstacle avoidance and dynamic goal-seeking.
• RL-augmented planning: Fuse learned cost shaping / value functions with trajectory optimization for smooth, agile flight with tight safety envelopes and mission constraints.
• Sim to Real at scale: Build scalable datasets and training loops with Isaac Lab, domain randomization, residual learning, and safety filters; validate on real drones weekly.
• Human-in-the-loop shared control: Learn assistive policies that blend pilot intent, autonomy priors, and uncertainty-aware behaviors for intuitive control handoffs.
• Fleet & multi-agent: Explore decentralized coordination for coverage, pursuit, and collaborative mapping with minimal comms.

What Makes This Internship Different

• Real hardware cadence: Prototype in sim, then flight test on production-ready aircraft and edge compute.
• Safety-first learning: Pair RL with formal constraints, estimation of uncertainty, and conservative action filters for dependable deployment in human environments.
• Cross-disciplinary mentorship: Collaborate with perception, mapping, controls, and product teams.

Requirements & Tech Stack

• Proficient in Python (PyTorch/JAX/Ray RLlib)
• Proficient in at least one of C++ or CUDA.
• Hands-on experience with robotics simulation (Isaac Lab/MuJoCo/PyBullet) and sim2real techniques.

Qualifications

• PhD student in Robotics, Machine Learning, Controls, or related field.
• Strong fundamentals in RL, control theory, and motion planning; comfort with safety/robustness concepts.
• Experience training/deploying policies for navigation, manipulation, or locomotion on real robots or autonomous vehicles.

Nice-to-Haves

• Publications (CoRL, ICRA, IROS, RSS, NeurIPS).
• Experience with onboard inference optimization (TensorRT, quantization, sparsity).
• Familiarity with modern policy learning beyond vanilla RL: diffusion policies, IL/BC, offline RL, model-based RL.
• Experience with multi-agent RL or distributed training.

Compensation

• Hourly rate: $47/hr for Undergrad, $53/hr for Masters, and $70/hr for PhD students.
• Compensation will vary based on factors including skill level, proficiencies, transferable knowledge, and experience.