Autonomy Engineer Intern, Reinforcement Learning, 2027 (Zurich)

Skydio builds the world’s most advanced autonomous drones used across inspection, public safety, defense, cinematography, and more. Your research won’t languish in a paper: it will fly, shaping 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.

Responsibilities

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; 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

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

Nice to have

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

Benefits

Skydio full-time employees are eligible to enroll in our benefit plans and take advantage of a variety of incentives and stipends