Research Engineer for Large Time-Series Models (f/m/div.)

- Completed Masters degree in Computer Science, Mathematics, Physics, Technical Cybernetics, or a related field, supplemented by a PhD in Machine Learning or a machine-related field - Demonstrated proficiency in scalable machine learning architectures and distributed training (including multi-GPU/multi-node training) - Several years of experience in AI projects focused on large-scale time-series data from industrial domains (sensors, physical systems, IoT devices, simulations, etc.) - Proven ability to translate theoretical methods into practical, high-quality code for scalable machine learning - Profound experience in large-scale data processing in an industrial environment - Excellent knowledge of MLOps (e.g. CI/CD pipelines, experiment tracking, containerization) in an industrial setting - Fluent in statistical methods including time-series analysis, scalability optimization, and the ability to translate theoretical methods into solutions for concrete engineering problems - Proven ability in software implementations of large-scale algorithms in Python/PyTorch and training and fine-tuning foundation models and large-language models is a plus - Ability to effectively communicate complex research results and collaborate seamlessly in cross-functional teams - Very good written and spoken English; German is a plus