Machine Learning Engineer (m/f/x)
ZEISS GroupBaden-Württemberg, Germany
Posted February 25, 2026
Full Time
About the Company
ZEISS is an internationally leading technology enterprise operating in the fields of optics and optoelectronics. In the previous fiscal year, the ZEISS Group generated annual revenue around 11 billion euros in its four segments Semiconductor Manufacturing Technology, Industrial Quality & Research, Medical Technology and Consumer Markets (30 September 2024). With over 46,000 employees, ZEISS is active globally in around 50 countries with more than 60 sales and service locations, around 40 research and development facilities, and 35 production facilities worldwide (30 September 2024). Founded in 1846 in Jena, the company is headquartered in Oberkochen, Germany. The Carl Zeiss Foundation, one of the largest foundations in Germany committed to the promotion of science, is the sole owner of the holding company, Carl Zeiss AG. Data privacy: www.zeiss.com/data-protection Imprint: http://zeiss.com/publisher This is ZEISS's official LinkedIn account. It follows the ZEISS Netiquette: www.zeiss.com/netiquette
Job Description
We are looking for a Machine Learning Engineer who can design, implement, and maintain a robust MLOps infrastructure, transform research code into production-grade software, and automate pipelines for testing, building, and deploying ML models. The ideal candidate has experience with Python, DevOps, and orchestration, and is familiar with MLOps tooling and CI/CD pipelines.
Requirements
- Engineering Foundation: University degree in Computer Science, Software Engineering, or a related technical field
- Python Expert: Deep proficiency in Python and clean code design
- DevOps & Orchestration: Experience with Docker and Kubernetes
- Infrastructure as Code (IaC): Experience with Terraform, Ansible, or Bicep
- Automation Specialist: Proven track record of designing and maintaining CI/CD pipelines
- MLOps Tooling: Familiarity with MLflow, Kubeflow, or DVC
- Project Leadership: Structured approach to work and experience in technical scope definition, backlog management, and coordinating with cross-functional teams
