Jobbeschreibung
At ZEISS Corporate Research & Technology, we work at the frontier of science and technology. Our mission is to innovate and develop intelligent solutions contributing directly to future ZEISS products. We're looking for a Machine Learning Engineer (f/m/x) who enjoys working across disciplines and is eager to develop intelligent systems that make a real difference for our consumers.
Integrated in a team of scientists and research engineers at ZEISS Corporate Research & Technologyyou will develop algorithms and support end-to-end machine learning lifecyclestaking ideas from academic and early stages to product launch.Working across the complete ZEISS productportfolio you will drive technology adoption and integration of latest advancements in machine learning, computer vision, imaging and optical metrology.Alongside the team, you will implement best practices to enhance the existing codebase and infrastructure with a focus on stability and scalability.You will actively research, develop, and promote best practices, contributing to knowledge exchange within the team and the broader ZEISS machine learning community.
During your work you will build an excellent network both within ZEISS and to external partners that help us to leverage the latest technology advancements to address tomorrow's challenges.
- An excellent university degreein computer science, engineering or similar– aPh.D. is a plus
- Strongproficiency in Python with professional software engineering experience (C++ and C# is a plus)
- Experience setting up CI/CD pipelines and container orchestration (Azure DevOps, Docker, Kubernetes is a plus)
- Skilled in Infrastructure as Code, cloud deployments, and automated infrastructure workflows (Azure, Ansible, Terraform is a plus)
- Familiarity with Machine Learning lifecycle tools (e.g. MLflow, Kubeflow, DVC)
- Strong project management capabilities—including scope definition, milestone planning, risk mitigation, product backlog maintenance and cross-functional coordination
- Understanding of Machine Learning (ML) algorithms and familiarity with modern Machine Learning libraries
- Hands-on mindset coupled with strong communication and presentation skills