Opportunities in Germany

About The Role

Agile Robots SE is a high-tech startup based in Munich. Our mission is to bridge the gap between AI and robotics by developing robotic systems that offer state-of-the-art full-body force sensitivity and world-leading vision intelligence. This unique combination of technologies enables us to provide intelligent, easy-to-use and affordable robotic solutions with safe human-robot interaction.

We are a dynamic and innovative software development company dedicated to pushing the boundaries of technology. We specialize in creating cutting-edge solutions that transform industries and redefine user experiences.

We are building humanoid robots that work reliably, autonomously, in the real world. As a Reinforcement Learning Engineer, you will own the development of end-to-end control policies for Agile One: from training in simulation to deployment on hardware, across locomotion, manipulation, and whole-body coordination in unstructured environments.

This is a high-ownership role. You will work at the frontier of what's possible with humanoid robots, make decisions with incomplete information, and iterate directly on physical systems. Success in this role requires both technical excellence and the ability to adapt and make decisions under uncertainty.

Your Responsibilities

• Design, maintain and deploy scalable, end-to-end training pipelines, including hyperparameter optimization

• Transfer control policies from simulation to real robotic hardware

• Integrate learned policies into a full-stack robotic system, including perception, planning, and actuation

• Analyze robot behavior and learning performance,e and iterate quickly based on real-world results

• Evaluate and benchmark state-of-the-art algorithms

• Stay updated with the latest research in RL and IL

• Work autonomously on open-ended, evolving tasks

Essential Skills

• Master’s or PhD in robotics, computer science, engineering, or a related field
• Proven experience (5+ years) applying RL or IL in physical systems, preferably on high DOF robots
• Familiarity with common RL algorithms (PPO, SAC, ...)
• Proficiency in Python and/or C++, PyTorch, ONNX, and common RL frameworks (e.g, Stable Baselines 3, RSL-RL)
• Experience with physics-based simulators (IsaacSim, MuJoCo or equivalent) for large-scale policy training
• Strong understanding of control theory, motion planning, and robot kinematics/dynamics
• Demonstrated success in sim-to-real transfer and deploying learning-based policies on physical robots
• Strong problem-solving skills and the ability to work independently in uncertain scenarios

Beneficial Skills

• Experience in training locomotion and locomanipulation policies
• Experience in training and deploying on humanoid robots
• Track record of publishing at CoRL, ICRA, RSS, NeurIPS, or ICLR

What we offer

• A dynamic high-tech company combined with financial soundness and world-class investors.
• Join an interdisciplinary, international team with 60+ different nationalities in a collaborative work environment.
• Lots of development opportunities in the context of our continued growth.
• Challenging tasks and impactful projects alongside experts that enable professional and personal growth.
• Corporate Benefits Program that covers health, mobility, and learning with 100€ net per month.
• Modern office facilities with a rooftop terrace overlooking Munich, free drinks & fruits, and regular company events contribute to a good working environment.

• Master’s or PhD in robotics, computer science, engineering, or a related field

• Proven experience (5+ years) applying RL or IL in physical systems, preferably on high DOF robots

• Familiarity with common RL algorithms (PPO, SAC, ...)

• Proficiency in Python and/or C++, PyTorch, ONNX, and common RL frameworks (e.g, Stable Baselines 3, RSL-RL)

• Experience with physics-based simulators (IsaacSim, MuJoCo or equivalent) for large-scale policy training

• Strong understanding of control theory, motion planning, and robot kinematics/dynamics

• Demonstrated success in sim-to-real transfer and deploying learning-based policies on physical robots

• Strong problem-solving skills and the ability to work independently in uncertain scenarios

• Experience in training locomotion and locomanipulation policies

• Experience in training and deploying on humanoid robots

• Track record of publishing at CoRL, ICRA, RSS, NeurIPS, or ICLR