About this role
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.
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Develop an efficient and reproducible workflow for application-oriented machine learning solutions.
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Be involved in sample preparation and data acquisition in the laboratory for full data ownership
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Implement quantitative metrics to assess and optimize data preprocessing quality and guide relabeling efforts
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Curate, structure, and version datasets for reproducible experiments
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Build data preprocessing pipelines for AI and machine learning workflows
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Generate and evaluate simulation-based or synthetic data
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Run hyperparameter and model studies in a traceable environment.
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Automate embedded-device tests and optimize accuracy and speed.
We Offer
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A dynamic and interdisciplinary environment
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Hands-on experience with real-world image, sensor, and simulation-based data, foundation models, model quantization, and edge deployment
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Learn how to set up a full machine learning pipeline
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Close mentorship from experts in deep learning
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An agile work environment
Your Profile
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Enrolled in a Bachelor’s or Master’s program in computer science, mathematics, physics, AI, or related fields
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Strong programming skills in Python and PyTorch
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Solid understanding of software development principles
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Fundamental knowledge of data preprocessing steps for computer vision and vision foundation models
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Familiarity with AI/ML concepts (e.g., supervised learning, deep learning architectures) is a plus
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Experience with edge-device deployment and model quantization is a plus
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Creative, pragmatic, and self-motivated with strong analytical skills
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Capable of working independently as well as in a team-oriented environment
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Excellent communication skills in English or German
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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