Internship in AI & Machine Learning

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. 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.

• 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