PhD Autonomy Engineer Intern - Planning & Controls (Reinforcement Learning) - Skydio - Etätyö - Globaali

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. Navigation & avoidance in the wild: Train policies that adapt online to cluttered 3D scenes (forests, bridges, urban canyons), complementing our geometric 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 → 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. Your work is meant to make a difference in the real world, not just in simulation. Safety-first learning: We 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 to close the loop from research to field ops. What makes you a strong fit: 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-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.