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

About TACTILIAAt TACTILIA, we are building the industry-ready 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