Machine Learning jobs in United Kingdom

At Corporate Research & Technology Department, we look at the innovations and developments of tomorrow. We research, analyse, advise and develop. All our ideas flow into future ZEISS products. We are looking for passionate and talented Machine Learning Engineer (f/m/x) to join our team and help drive the development of next-generation products and cutting-edge algorithms at ZEISS.

We are seeking passionate and talented machine learning engineers who want to drive the development of next generation products and cutting-edge machine learning solutions at ZEISS.

Integrated in a team of scientists and research engineers at ZEISS Corporate Research & Technology, you will develop algorithms and support end-to-end machine learning lifecycles, taking ideas from academic and early stages to product launch. Working across the complete ZEISS product portfolio, you will drive technology adoption and integration of latest advancements in machine learning, computer vision, imaging, and optical metrology. You will implement best practices to enhance the existing codebase and infrastructure with a focus on stability and scalability. Additionally, 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 with external partners to leverage the latest technology advancements for addressing tomorrow’s challenges.

• An excellent university degree in computer science, engineering or similar – a Ph.D. is a plus.
• Strong proficiency 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 algorithms and familiarity with modern Machine Learning libraries.
• Hands-on mindset with strong communication and presentation skills.

Friederike Kirklar-Harms