Machine Learning Engineer m/f/d

About Us JSP is quoted on the Tokyo stock exchange and is a worldwide leader and specialist in EPP manufacturing and plastic transformation (injection moulding, extrusion, blow moulding) delivering solutions for many sectors including Automotive, HVAC, transportation, sporting goods and more. We are looking for a Machine Learning Engineer to join our team and design, build, and continuously improve machine learning models for AI‑driven manufacturing solutions within an Industry 4.0 environment. In this role, you will turn manufacturing data into reliable, high‑performing models that can be deployed at scale and reused across multiple use cases, machines, and plants.

The role needs you to:

Design, train, and evaluate machine learning models for manufacturing use cases such as quality prediction, anomaly detection, predictive maintenance, and process optimisation, covering the full ML lifecycle from problem definition to performance tuning. Develop reusable, well‑documented ML models and components, working closely with Data Engineers to ensure reliable data and delivering production‑ready models for integration into AI agents and manufacturing applications. Ensure Machine learning solutions are developed and deployed in line with information security, data protection, access control, and traceability requirements. Monitor and improve model performance in real‑world environments, applying best practices in experimentation, versioning, reproducibility, and selecting appropriate ML techniques (classical ML, deep learning, time‑series) based on the use case. Contribute to documentation, development standards, and machine learning best practices across the organization. Following essential assets will drive your success in the role: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Applied Mathematics, or a related field. Strong hands-on experience developing machine learning models using Python and common frameworks (e.g. PyTorch, TensorFlow, scikit-learn). Solid understanding of machine learning principles, including supervised and unsupervised learning, model evaluation, and performance optimisation. Awareness of secure development practices and information security requirements in industrial data and AI/ML environments. Experience working with time-series, sensor, image, or structured industrial data. Familiarity with model validation, monitoring, and testing in production or near-production environments. Ability to translate manufacturing or business challenges into well-defined machine learning tasks with measurable outcomes. Experience collaborating with Data Engineers and AI or software engineers in cross-functional teams. Familiarity with cloud-based ML platforms (e.g. Microsoft Azure) is advantageous. Strong analytical skills, attention to detail, and a practical approach to delivering reliable ML solutions. Clear and structured communication skills, with the ability to explain machine learning concepts, results, and trade-offs to both technical and non-technical audiences. We are offering: A permanent full-time contract in a sustainable, innovative, and future-oriented company A careful onboarding plan, supported by manager and cross functional colleagues Diverse, interesting and challenging tasks, opportunities for further development Room to contribute with own ideas and space for creativity to achieve your goals 30 vacation days per year, flexible time, home office Sports allowance, pension contribution, company bike,... Weitere Informationen JSP EMEA is a subsidiary of JSP Tokyo, benefits from a worldwide business environment and has more than 400 employees spread over 4 main sites (Czech Republic, France, Germany and Italy). If you are enthusiastic about applying machine learning to real manufacturing challenges in an Industry 4.0 environment and enjoy working collaboratively across engineering teams, please send your application including CV & cover letter in English as well as earliest start date and salary expectation to . Deadline for application: 30th of September 2026