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# (Senior) Robotics Software Engineer - Reinforcement Learning

- **Company:** Agile Robots Ag
- **Location:** München, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, C++ (Programming Language), Python (Programming Language), Kinematics, Motion Planning, Reinforcement Learning, Robot Operating System, Pytorch, Information Technology, ONNX (Open Neural Network Exchange) Format
- **Published:** July 2, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=38bfd9c403a7e18d

## About the Role

* physics-basedMaster'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

## Description

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


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