Machine Learning Engineer (f/m/x)

ZEISS Group; Oberkochen; Vor Ort; EUR 50.000 - 80.000; Vollzeit

Zusammenfassung

A leading technology company in Germany is seeking a passionate Machine Learning Engineer to develop algorithms and support machine learning lifecycles. You will work in a team to drive innovation, enhance existing solutions, and contribute to a vibrant research community. Strong programming skills in Python and experience in CI/CD and container orchestration are essential. This role offers an exciting chance to shape future products at the company.

Qualifikationen

• Excellent degree in computer science, engineering or similar; Ph.D. is a plus.
• Strong proficiency in Python with software engineering experience; C++ and C# is a plus.
• Experience with CI/CD pipelines and container orchestration.
• Skilled in Infrastructure as Code and cloud deployments.
• Familiarity with Machine Learning lifecycle tools.
• Strong project management capabilities.
• Understanding of Machine Learning algorithms.
• Hands-on mindset with strong communication skills.

Aufgaben

• Develop algorithms and support end-to-end machine learning lifecycles.
• Drive technology adoption and integration of advancements in machine learning.
• Enhance existing codebase with a focus on stability and scalability.
• Contribute to knowledge exchange within the team.

Kenntnisse

Python

C++

C#

CI/CD Pipelines

Container Orchestration

Infrastructure as Code

Machine Learning Algorithms

Project Management

Ausbildung

Computer Science degree

Ph.D.

Tools

Azure DevOps

Docker

Kubernetes

Azure

Ansible

Terraform

MLflow

Kubeflow

DVC

Jobbeschreibung

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.

Your Role

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.

Your Profile

• 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.

Your ZEISS Recruiting Team

Friederike Kirklar-Harms