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

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Skydio

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Location:
Switzerland , Zurich

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Category:
Research and Development

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Contract Type:
Not provided

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Salary:

50.00 EUR / Hour
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Job Description:

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.

Job Responsibility:

  • 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

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

Additional Information:

Job Posted:
January 03, 2026

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