Internship in AI & Machine Learning (f/m/x)
Zeiss Group Open · verified Nov 1, 2026 At a glance
Junior Machine Learning Engineering role at Zeiss Group. Karlsruhe · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Collect laboratory data and prepare samples so you can take ownership of the vision data
- Build preprocessing and dataset workflows, including quality measures that identify when labels need revision
- Create synthetic or simulation-based data, then assess how well it supports the application
- Compare models and settings in a traceable setup, and evaluate their performance
- Automate tests on embedded devices and improve model speed and accuracy
What you bring
- You are enrolled in a bachelor’s or master’s program in computer science, mathematics or another listed field
- You program confidently in Python and PyTorch and understand sound software development practices
- You know core data preparation for computer vision and vision foundation models
- You can communicate effectively in English or German
Who this fits
This internship suits a student interested in practical computer vision work across data, model development and deployment. You’ll work independently as well as with a team, with mentorship from deep learning experts. The role is based in Karlsruhe, Germany.
From the employer
Motivation of the Work
Turning today’s research into tomorrow’s applications, together. At ZEISS, we focus on user-centric innovation to transform ideas into cutting-edge solutions. The ZEISS Innovation Hub @ KIT brings together students, researchers, and industry professionals to advance AI, computer vision, and machine learning in application-oriented projects.
Your Role
We are looking for highly motivated students to develop AI-based solutions for real-world vision tasks, including agricultural solutions, from data collection, simulation-based data generation, and model development to deployment on target devices. This internship provides hands-on experience across the full ML pipeline, including vision foundation models, data preprocessing and curation, quantization-aware training, edge-device deployment, and performance evaluation.
- Develop an efficient and reproducible workflow for application-oriented machine learning solutions.
- Be involved in sample preparation and data acquisition in the laboratory for full data ownership
- Implement quantitative metrics to assess and optimize data preprocessing quality and guide relabeling efforts
- Curate, structure, and version datasets for reproducible experiments
- Build data preprocessing pipelines for AI and machine learning workflows
- Generate and evaluate simulation-based or synthetic data
- Run hyperparameter and model studies in a traceable environment.
- Automate embedded-device tests and optimize accuracy and speed.
We Offer
- A dynamic and interdisciplinary environment
- Hands-on experience with real-world image, sensor, and simulation-based data, foundation models, model quantization, and edge deployment
- Learn how to set up a full machine learning pipeline
- Close mentorship from experts in deep learning
- An agile work environment
Your Profile
- Enrolled in a Bachelor’s or Master’s program in computer science, mathematics, physics, AI, or related fields
- Strong programming skills in Python and PyTorch
- Solid understanding of software development principles
- Fundamental knowledge of data preprocessing steps for computer vision and vision foundation models
- Familiarity with AI/ML concepts (e.g., supervised learning, deep learning architectures) is a plus
- Experience with edge-device deployment and model quantization is a plus
- Creative, pragmatic, and self-motivated with strong analytical skills
- Capable of working independently as well as in a team-oriented environment
- Excellent communication skills in English or German
- Passion for innovation, enthusiasm for new technologies, and motivation to contribute to interdisciplinary projects
Sounds exciting? Then become part of #teamZEISS and help us shape the future! Please provide your complete application documents (CV, transcript of records, etc.).
Your ZEISS Recruiting Team:
Selina Safradin
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