Autonomy Engineer Intern (Reinforcement Learning)

Location: Zurich, Switzerland; internship based in Zurich

Compensation: $47/hour for undergraduate students, $53/hour for master's students, and $70/hour for PhD students. Compensation varies based on skill level, proficiencies, transferable knowledge, and experience.

Company

hirify.global develops autonomous drones and flight technology for inspection, public safety, defense, cinematography, and other real-world applications.

What you will do

• Develop and deploy reinforcement-learning and related policy-learning methods for autonomous drone planning, navigation, and control.
• Train policies for obstacle avoidance, dynamic goal-seeking, and navigation in cluttered 3D environments.
• Combine learned cost shaping and value functions with trajectory optimization for agile flight within safety and mission constraints.
• Build scalable datasets and training loops using Isaac Lab, domain randomization, residual learning, and safety filters, then validate them on real drones.
• Develop human-in-the-loop shared-control policies and explore decentralized multi-agent coordination for coverage, pursuit, and collaborative mapping.

Requirements

• Current PhD student in Robotics, Machine Learning, Controls, or a related field.
• Strong fundamentals in reinforcement learning, control theory, and motion planning, with familiarity with safety and robustness concepts.
• Proficiency in Python and experience with PyTorch, JAX, or Ray RLlib.
• Proficiency in at least one of C++ or CUDA.
• Hands-on experience with robotics simulation, including Isaac Lab, MuJoCo, or PyBullet, and sim-to-real techniques.
• Experience training and deploying policies for navigation, manipulation, or locomotion on real robots or autonomous vehicles.

Nice to have

• Publications at CoRL, ICRA, IROS, RSS, or NeurIPS.
• Experience optimizing onboard inference with TensorRT, quantization, or sparsity.
• Familiarity with policy-learning methods beyond vanilla reinforcement learning, including diffusion policies, imitation learning or behavioral cloning, offline reinforcement learning, and model-based reinforcement learning.
• Experience with multi-agent reinforcement learning or distributed training.

Culture & Benefits

• Prototype in simulation and flight-test on production-ready aircraft and edge compute.
• Apply safety-first learning with formal constraints, uncertainty estimation, and conservative action filters.
• Collaborate with perception, mapping, controls, and product teams across the path from research to field operations.
• Work in a multidisciplinary and inclusive environment that values diverse perspectives.
• Full-time employees are eligible for benefit plans and various incentives and stipends.