At Bosch, we are on a mission to harness the power of large-scale time-series data to optimize industrial processes and create innovative solutions. We are building the next generation of foundation models for time-series to be utilized across the various fields of the Bosch group, such as manufacturing, mobility solutions, energy systems, and IoT applications.

• As part of our team, you'll help push the state-of-the-art in model scalability by contributing to the core systems for our products.
• As a Research Engineer at Bosch, you will focus on the efficient implementation of scalable architectures for large-scale time-series models. Your primary responsibility will be to optimize training and inference pipelines, enhancing their performance in terms of runtime and memory usage.
• You will work on applying and refining these scaled models for a diverse array of industrial use cases, leveraging massive datasets from Bosch's proprietary sources to solve real-world challenges.
• The design and optimization of training and inference pipelines for time-series foundation models will be your responsibility.
• With foresight and precision, you will develop efficient algorithms for processing and analyzing massive industrial datasets.
• You will research and develop scalable architectures for large-scale time-series modeling in an industrial environment, aiming to set new standards.
• Continuously, you will drive the performance optimization and benchmarking of time-series models to ensure maximum efficiency.
• In close collaboration with engineering teams and business units, you will implement and deploy scalable solutions for industrial time-series applications.
• Your research results will be published and patented, securing and sharing our technological advantage.

• ​​​​​​Education: completed Master's degree in Computer Science, Mathematics, Physics, Technical Cybernetics, or a related field, supplemented by a PhD in Machine Learning or a machine-related field
• Experience and Knowledge:
• • 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 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 as well as to train and fine-tune foundation models and large-language models is a plus
• Personality and Working Practice: you effectively communicate complex research results and collaborate seamlessly in cross-functional teams
• Languages: very good written and spoken English, German is a plus