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

Location Zurich, Switzerland Employment Type Intern Location Type Hybrid Department R 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 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. #LI-PG1 At Skydio we believe that diversity drives innovation. We have created a multidisciplinary environment that embraces the power of diverse perspectives to create elegant solutions for complex problems. We are committed to growing our network of people, programs, and resources to nurture an inclusive culture. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or other characteristics protected by federal, state or local anti-discrimination laws. For positions located in the United States of America, Skydio, Inc. uses E-Verify to confirm employment eligibility. To learn more about E-Verify, including your rights and responsibilities, please visit https://www.e-verify.gov/ #J-18808-Ljbffr