Machine Learning / AI Developer for Industrial Laser Processes - all genders)

Machine Learning / AI Developer for Industrial Laser Processes (m/f/d - all genders) 85748 Garching, 80339 Munich Full-time Permanent Blackbird Robotersysteme GmbH and Lessmüller Lasertechnik GmbH are working in close technological partnership to develop innovative solutions for process monitoring, seam guidance, and the automation of laser welding processes. To further expand our joint development activities in the field of data-driven processes, we are seeking a skilled professional in the field of AI as a Machine Learning / AI Developer for Industrial Laser Processes (m/f/d - all genders) Your Responsibilities: Building and advancing expertise in the field of machine learning / AI Creation, preparation, and evaluation of training data in collaboration with the application team and the laser lab Development, training, and validation of machine learning models for industrial applications Integration of the developed models into existing software solutions Close collaboration with internal departments as well as related development and application teams Your Profile: Bachelor’s or master’s degree in computer science, physics, mechanical engineering, mechatronics, or a comparable qualification Professional experience in machine learning/AI for industrial applications Experience with real-world sensor, vision, or time-series data Very good understanding of physical and technical processes, ideally in the fields of laser technology, optics/photonics, laser material processing, or other sophisticated manufacturing processes Experience in data-driven process optimization and model validation under real-world conditions Practical experience with common machine learning and deep learning frameworks, particularly scikit-learn, PyTorch, TensorFlow, ONNX, or comparable technologies Good command of German and English Experience collaborating with interdisciplinary teams or industry partners Ability to work well in a team, as well as an independent and structured approach to