The posting, in CARIAD's own words
archived Oct 24, 2026We are CARIAD , an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year. We ’ re looking for talented, digital minds like you to help us create code that moves the world. Together with you, we ’ ll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it. We are CARIAD , an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year. We’re looking for talented, digital minds like you to help us create code that moves the world. Together with you, we’ll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it.
Role Summary:
The Engineer I, VLA Model Development (Intern) will support model research and development for autonomous driving at CARIAD INC. and the Volkswagen Group. Working with researchers and engineers, this role will develop and evaluate vision-language-action (VLA) models that connect visual observations and language with driving actions. Depending on project needs and research strengths, the work may focus on perception, scene understanding, or world models that predict how driving environments evolve.
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Role Responsibilities:
VLA Model and Development Prototype, train, and fine-tune VLA models or supporting perception and world-model components. Investigate recent methods and implement a focused research project with mentor guidance. Data and Experimentation Prepare driving datasets and evaluation splits; improve data quality and coverage. Run controlled experiments and ablations and maintain reproducible configurations and experiment records. Evaluation and Integration Measure model accuracy, driving behavior, robustness, and inference cost. Analyze failures in offline and simulation evaluation; collaborate with planning and deployment engineers on model interfaces. Documentation and Collaboration Document methods, results, and limitations; review code and present findings. Deliver a reproducible prototype and a final technical report.