Internship in AI & Machine Learning (f/m/x)

ZEISS Group; Karlsruhe; Sur place; EUR 8 900 - 13 000; Plein temps

Résumé du poste

ZEISS Innovation Hub @ KIT invites motivated students to develop AI-based solutions for real-world vision tasks, spanning data collection, simulation data generation, model development, and deployment on edge devices.

You'll build end-to-end ML pipelines, work with vision foundation models, and contribute to data preprocessing, quantization-aware training, and performance evaluation in a collaborative, mentoring environment.

Qualifications

• Bachelor's or Master’s student in CS, math, physics, AI or related fields.
• Strong Python and PyTorch programming skills.
• Solid software development fundamentals.
• Familiarity with CV preprocessing steps and vision foundation models.
• Experience with edge deployment and model quantization is a plus.
• Excellent communication in English or German.

Responsabilités

• Develop AI-based solutions for real-world vision tasks.
• Handle data collection, simulation data generation, and model development to deployment.
• Build ML pipelines including data preprocessing, quantization-aware training and edge deployment.
• Automate tests and evaluate performance.
• Collaborate with ZEISS team and document experiments.

Connaissances

Python

Data preprocessing

Vision foundation models

English/German communication

Formation

Bachelor's/Master's student in CS/Math/Physics/AI

Outils

PyTorch

Description du poste

Motivation of the WorkTurning 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 RoleWe 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 ownershipImplement quantitative metrics to assess and optimize data preprocessing quality and guide relabeling effortsCurate, structure, and version datasets for reproducible experimentsBuild data preprocessing pipelines for AI and machine learning workflowsGenerate and evaluate simulation-based or synthetic dataRun hyperparameter and model studies in a traceable environment.Automate embedded-device tests and optimize accuracy and speed.We OfferA dynamic and interdisciplinary environmentHands-on experience with real-world image, sensor, and simulation-based data, foundation models, model quantization, and edge deploymentLearn how to set up a full machine learning pipelineClose mentorship from experts in deep learningAn agile work environmentYour ProfileEnrolled in a Bachelor’s or Master’s program in computer science, mathematics, physics, AI, or related fieldsStrong programming skills in Python and PyTorchSolid understanding of software development principlesFundamental knowledge of data preprocessing steps for computer vision and vision foundation modelsFamiliarity with AI/ML concepts (e.g., supervised learning, deep learning architectures) is a plusExperience with edge-device deployment and model quantization is a plusCreative, pragmatic, and self-motivated with strong analytical skillsCapable of working independently as well as in a team-oriented environmentExcellent communication skills in English or GermanPassion for innovation, enthusiasm for new technologies, and motivation to contribute to interdisciplinary projectsSounds 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