Machine Learning Engineer (m/f/x)

Responsibilities

• Architect the ML Platform: Design, implement, and maintain a robust MLOps infrastructure that enables researchers to move seamlessly from local experimentation to global production.

• Productionalize Research: Act as the \"Engineering Bridge\" by transforming experimental research code into modular, high-performance, and maintainable Python/C++ software packages.

• Automation & CI/CD: Build and manage sophisticated automated pipelines for testing, building, and deploying ML models across diverse ZEISS product environments (Cloud, Edge, and On-premise).

• Infrastructure as Code (IaC): Own the provisioning and scaling of our research computing environments using Terraform and Ansible, ensuring high availability and resource efficiency.

• Establish Engineering Standards: Define and promote best practices for the entire department, including version control (Git), containerization (Docker), code quality (linting/testing), and documentation.

• Observability & Lifecycle Management: Implement advanced monitoring and logging solutions (e.g., MLflow, ELK stack) to track model performance, data drift, and system health in real-world applications.

• Collaborative Consulting: Serve as the internal expert and consultant for scientists, helping them optimize their workflows and navigate the complexities of modern cloud and hardware environments.

Qualifications

• Excellent university degree in Computer Science, Software Engineering, or a related technical field.

• Deep proficiency in Python and an advocate for clean code, design patterns, and modular architecture.

• Experience with C++ or C# is a significant advantage for integrating ML into high-performance hardware systems.

• Hands‑on experience with Docker and Kubernetes, comfortable managing containerized workloads and scaling services in a corporate environment.

• Skilled in automating infrastructure using Terraform, Ansible, or Bicep within the Azure ecosystem.

• Proven track record of designing and maintaining CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) that go beyond simple builds to include automated testing and deployment.

• Familiar with, or eager to master, the ML lifecycle stack, such as MLflow, Kubeflow, or DVC, and understand how to apply standard DevOps principles to machine‑learning challenges.

• Strong communicator who can mentor researchers on engineering best practices without stifling their creativity.

• Experience with technical scope definition, backlog management, and coordinating with cross‑functional teams.

This position is not remote.

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Diversity is a part of ZEISS. xayajpt We look forward to receiving your application regardless of gender, nationality, ethnic and social origin, religion, philosophy of life, disability, age, sexual orientation or identity.