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# AI Software Engineer, Reinforcement Learning

- **Company:** TACTILIA robotics
- **Location:** München, Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Learning Management Systems, Python (Programming Language), Kinematics, Machine Learning, Robotic Automation Software, Software Engineering, Reinforcement Learning, Pytorch, Deep Learning, Information Technology
- **Published:** September 23, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=1e22a2d6d4d1fd23

## About the Role

* Degree in Computer Science, Robotics, AI, Electrical Engineering or a related field
 * Strong software engineering skills in Python and preferably C++
 * Hands-on experience with reinforcement learning and deep learning
 * Strong understanding of RL concepts such as policy optimisation, value functions, exploration and reward design
 * Experience with PyTorch or a similar deep learning framework
 * Experience working with robotic systems and simulation environments
 * Good understanding of robot kinematics, dynamics and control
 * Strong analytical and problem-solving skills
 * A hands-on mindset and enthusiasm for testing learning algorithms on real robotic systems

Bonus

 * Experience with dexterous manipulation, grasping or robotic hands
 * Experience with sim-to-real reinforcement learning
 * Experience with PPO, SAC or other modern RL algorithms
 * Experience with Isaac Lab, Isaac Sim, MuJoCo or similar environments
 * Experience with distributed or large-scale RL training

## Description

As an AI Software Engineer focused on Reinforcement Learning, you will develop the learning systems that enable TACTILIA's robotic hands to acquire complex manipulation skills through interaction and experience.

You will work across reinforcement learning, robotics and simulation to develop policies that can learn complex behaviours and ultimately perform them reliably on real hardware. Your work will help push dexterous manipulation beyond hand-engineered behaviours towards scalable, adaptive robotic skills.

What you will do:

 * Develop reinforcement learning algorithms for dexterous manipulation and robotic hands
 * Design training environments, reward functions and learning curricula for complex manipulation tasks
 * Train and evaluate policies in simulation and on real robotic hardware
 * Develop methods for efficient exploration, learning and policy optimisation
 * Work on sim-to-real transfer and robustness of learned policies
 * Build software infrastructure for large-scale training, evaluation and experimentation
 * Analyse policy behaviour and failure modes and use these insights to improve training
 * Integrate learned policies with robotics and control systems
 * Work closely with simulation, controls and robotics engineers to bring learned behaviours onto the real hand
 * Contribute to scalable approaches for learning new manipulation skills


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