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

Tactiliarobotics · Munich

Publicada em 24/09/2026

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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 Imitation Learning, you will develop the learning systems that enable TACTILIA's robotic hands to acquire dexterous manipulation skills from demonstrations.You will work across data, machine learning and robotics to turn human demonstrations and robot trajectories into robust manipulation policies that can run on real hardware. Your work will help build the foundation for scalable skill learning and bring Physical AI from demonstrations into reliable real-world behavior.What you will do:Develop imitation learning methods for dexterous manipulation and robotic handsBuild data pipelines for collecting, processing and curating demonstrations and robot trajectoriesDevelop and train policies from human and robot demonstrationsWork with multimodal data including robot states, actions, vision and tactile signalsEvaluate, debug and improve learned policies on simulation and real robotic hardwareDevelop methods for data efficiency, generalisation and robust policy executionBuild software infrastructure for training, evaluation and deployment of learned behavioursWork closely with robotics and controls engineers to integrate learned policies with t
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