Skydio
Drones
PhDAutonomyEngineerIntern-Planning&Controls(ReinforcementLearning)
Neural analysis suggests this role is
optimal for Entry candidates.
“PhD Autonomy Engineer Intern - Planning & Controls (Reinforcement Learning) at Skydio. Skills: Reinforcement Learning, Autonomous flight, Robotics. Develop reinforcement learning policies. Deploy reinforcement learning policies”
What You'll Achieve.
make Skydio aircraft plan, navigate, and control themselves more intelligently; safely, reliably, and efficiently; robust obstacle avoidance; dynamic goal-seeking; smooth, agile flight; tight safety envelopes; mission constraints; safety validate on real drones weekly; intuitive control handoffs; minimal comms; make a difference in the real world; dependable deployment in human environments; close the loop from research to field ops
Industry & Context.
Navigation & avoidance in the wild; RL-augmented planning; Sim → Real at scale; Human-in-the-loop shared control; Fleet & multi-agent
What They're Looking For.
Must Have
PhD student in Robotics, Machine Learning, Controls, or related field, fundamentals in RL, control theory, and motion comfort with safety/robustness concepts, Proficient in Python (PyTorch/JAX/Ray RLlib), at least one of C++ or CUDA, Hands-on experience with robotics simulation (Isaac Lab/MuJoCo/PyBullet), 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, ILC, offline RL, model-based RL, Experience with multi-agent RL or distributed training
What You'll Do.
Develop reinforcement learning policies
Deploy reinforcement learning policies
Train policies for navigation
Train policies for avoidance
Fuse learned cost shaping
Build scalable datasets
Learn assistive policies
Explore decentralized coordination
Flight test on aircraft
Flight test on edge compute
How You'll Work.
Team & Collaboration
Collaborate with perception teams; Collaborate with mapping teams; Collaborate with controls teams; Collaborate with product teams
Full Job Description
Skydio is the leading US drone company and the world leader in autonomous flight, the key technology for the future of drones and aerial mobility. The Skydio team combines deep expertise in artificial intelligence, best-in-class hardware and software product development, operational excellence, and customer obsession to empower a broader, more diverse audience of drone users, from utility inspectors https://www.skydio.com/solutions/energy-and-utilities to first responders https://www.skydio.com/solutions/public-safety, soldiers in battlefield scenarios https://www.skydio.com/solutions/national-security/tactical-isr, and beyond https://www.skydio.com/solutions. About the role: 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. HOW YOU'LL MAKE AN IMPACT: - 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 cont
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