Reinforcement Learning Research Engineer – Exploration & Decision Intelligence (m/w/d)

What we offer:
• Opportunity to work on a new solution from scratch in a technical complex environment
• Work in an international, agile, cross-functional team creating the future of autonomous systems
• Grow your career in a expanding and ambitious engineering team
• Build innovative products using state-of-the-art technologies in AI, robotics, and autonomy 
• Benefit from a steep learning curve and continuous development
• Enjoy team events and a strong, collaborative culture

Your mission:
Build real autonomous systems that operate in the real world, not in the lab. 

Join our engineering team of a new product and help build the core autonomy that powers our next generation robotic systems used for defense and mission-critical operations. You will design, implement, and harden robotic software that must perform under real operational conditions - outdoors, under uncertainty, with real consequences. Your work will directly shape the reliability, safety, and tactical capability of the systems we deliver. 
 
• Research and prototype novel RL algorithms (e.g. exploration, POMDPs, multi-agent systems) 
• Define, design and implement use-cases for DRL on edge devices 
• Translate theory into scalable systems with support from our engineering teams 
• Collaborate with simulation, autonomy and AI infrastructure teams 
• Develop decision-making for intelligent behavior and architectures

Your profile:
• Deep knowledge of RL theory and practice: policy gradients, value iteration, Q-learning, etc. 
• Experience with ML training in physics based simulation (Gazebo, IsaacSim, Mujoco, Carla, etc.).
• Strong Programming proficiency (Python, C/C++).
• Comfortable with ML tooling and maintaining ML pipelines (Pytorch Lightning, MlFlow, etc.).
• Have experience with deploying ML methods to physical devices.
• Experience with version control (git).
• Familiarity with statistics, evaluation methods and experiment design.
• You think rigorously and build practically.

Nice to have:
• PhD in Reinforcement Learning, Robot Engineering or equivalent with experience in deploying developed methods to real robots.
• OR masters degree in relevant field with extensive experience in RL.
• Experience with sensor based end-to-end ML architectures.
• Familiar with Transformers, Attention, Graphs, VLAs and other modern day ML building blocks.
• Publications at NeurIPS, ICLR, ICML, ICRA, IROS, etc. are a plus 
• Experience with robotics middleware (ISAAC, ROS/ROS2, etc.)

Why us?:
• Willingness to travel
• Citizenship of NATO member country or closed allied are mandatory