AI Software Engineer, Reinforcement Learning (f/m/d)

eady robotic hand that closes one of the biggest gaps in Physical AI. Spun out of SCHUNK, the global market leader in gripping technology, we combine a decade of robotic-hand expertise and real industrial access with the speed and ambition of a deep-tech startup. This isn't a research project waiting for its first customer. We already have a product, customers are ready to use it, and we are launching now. Your work will directly shape the electronics, actuators, and embedded systems that make that possible. You will join at the moment when the hard engineering questions become real product decisions: how do we make a highly capable robotic hand reliable, manufacturable, serviceable, and simple enough to deploy on a real shop floor? The Role: 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 handsDesign training environments, reward functions and learning curricula for complex manipulation tasksTrain and evaluate policies in simulation and on real robotic hardwareDevelop methods for efficient exploration, learning and policy optimisationWork on sim-to-real transfer and robustness of learned policiesBuild software infrastructure for large-scale training, evaluation and experimentationAnalyse policy behaviour and failure modes and use these insights to improve trainingIntegrate learned policies with robotics and control systemsWork closely with simulation, controls and robotics engineers to bring learned behaviours onto the real handContribute to scalable approaches for learning new manipulation skills What you bring: Degree in Computer Science, Robotics, AI, Electrical Engineering or a related fieldStrong software engineering skills in Python and preferably C++Hands-on experience with reinforcement learning and deep learningStrong understanding of RL concepts such as policy optimisation, value functions, exploration and reward designExperience with PyTorch or a similar deep learning frameworkExperience working with robotic systems and simulation environmentsGood understanding of robot kinematics, dynamics and controlStrong analytical and problem-solving skillsA hands-on mindset and enthusiasm for testing learning algorithms on real robotic systems Bonus Experience with dexterous manipulation, grasping or robotic handsExperience with sim-to-real reinforcement learningExperience with PPO, SAC or other modern RL algorithmsExperience with Isaac Lab, Isaac Sim, MuJoCo or similar environmentsExperience with distributed or large-scale RL training The next chapter of robotics won't be built in a lab. It will be built by people who care about what happens when technology meets the real world. At TACTILIA, you'll work on one of the hardest open problems in robotics: giving machines the dexterity, reliability and robustness to interact with the physical world at industrial scale. The technology is taking shape, the team is being built, and the standards around robotic hands and their skills are still open. That means your decisions will have a lasting impact — not just on a product, but on what comes next for Physical AI. Build the hand. Define the standard. Shape what comes next. If you're excited by the challenge of building robotic hands that works reliably in the real world, we'd love to hear from you. Skills: R&D