Autonomy RL Research Intern

Perot Jain; Zürich; Hybrid; CHF 54,000 - 80,000; Part time

Job summary

Skydio is seeking a PhD-level intern to develop reinforcement learning methods for autonomous drones. You will train navigation and obstacle avoidance policies, and build scalable sim-to-real pipelines across perception, mapping, controls, and product teams.

Real hardware flight tests will accompany prototyping to ensure dependable deployment. Ideal candidates have strong RL fundamentals, experience with Python ML stacks, and hands-on robotics simulation (Isaac Lab, MuJoCo, PyBullet).

Qualifications

• PhD student in Robotics, ML, Controls or related field.
• Strong fundamentals in RL, control theory and motion planning.
• Proficient in Python (PyTorch/JAX/Ray RLlib) and one of C++ or CUDA.

Responsibilities

• Develop and deploy reinforcement learning methods for Skydio drones.
• Train policies for navigation, obstacle avoidance, and robust flight.
• Build and validate sim-to-real pipelines with simulation and real tests.
• Collaborate with perception, mapping, controls, and product teams.
• Contribute to fleet and multi-agent coordination research.

Skills

RL fundamentals

Python (PyTorch/JAX/RLlib)

Sim2Real techniques

Navigation policy training

Education

PhD student in Robotics/Machine Learning/Controls

Tools

Isaac Lab

MuJoCo

PyBullet

TensorRT

Job description

Skydio is seeking a PhD-level intern to develop reinforcement learning methods for autonomous drones. You will train navigation and obstacle avoidance policies, and build scalable sim-to-real pipelines across perception, mapping, controls, and product teams.

Real hardware flight tests will accompany prototyping to ensure dependable deployment. Ideal candidates have strong RL fundamentals, experience with Python ML stacks, and hands-on robotics simulation (Isaac Lab, MuJoCo, PyBullet).