# AI & Computer Vision Engineering Intern at HP

HP is hiring an AI & Computer Vision Engineering Intern in Barcelona, Spain. Level rates it Builds AI ●●●●; you can [apply on HP](https://jobsbylevel.com/go/f5a6ecb6-5730-449d-bc88-958cbdf63ec9?utm_medium=ai-agent).

AI Level 4, AI centrality 86 out of 100. Sant Cugat del Valles, Barcelona, Spain.

## Details

- Company: [HP](https://jobsbylevel.com/companies/hp)
- AI level: AI Level 4 (score 86 out of 100)
- Location: Sant Cugat del Valles, Barcelona, Spain
- Posted: October 29, 2026
- Apply: https://jobsbylevel.com/go/f5a6ecb6-5730-449d-bc88-958cbdf63ec9?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-computer-vision-engineering-intern-at-hp-7afcd8

## Prepare for this job

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## Description

AI & Computer Vision Engineering Intern Description - Before applying please note that, to be eligible for this position, you must be currently enrolled at a university in Spain , remain enrolled until the end of the internship, and be eligible to sign an internship agreement with your university. You must also be available to start the internship in February 2027 and continue participating in the program until June–July 2027 . About the role Join HP Additive Manufacturing (HPAM), one of HP's most innovative and fast-growing businesses, where breakthrough technologies are transforming the future of digital manufacturing. This is a unique opportunity to contribute to next-generation industrial 3D printing solutions while working at the intersection of artificial intelligence, computer vision, and additive manufacturing. As part of a highly collaborative organization, you will work with experts across R&D, Quality, Manufacturing, and Data Science to develop an AI-powered visual inspection solution for 3D printed parts. The goal is to automate the evaluation of print quality and detect manufacturing defects through images, helping improve consistency, efficiency, and scalability of quality assessment processes. This internship offers hands-on experience with real-world industrial challenges, including building image acquisition strategies, creating and annotating datasets, defining defect taxonomies, and developing machine learning models capable of identifying defects such as porosity, bubbles, alignment issues, elephant skin, and other surface anomalies. You will have the opportunity to work on a project that could evolve from an internal efficiency tool into a customer-facing capability integrated into future HP 3D printing workflows. Key responsibilities: Design and implement image acquisition strategies for capturing 3D printed parts under controlled and repeatable conditions. Build a high-quality computer vision dataset, including image collection, organization, and annotation. Develop computer vision and deep learning models to automatically identify and classify print quality defects. Evaluate and compare classification, object detection, and segmentation approaches. Develop image preprocessing, augmentation, and data preparation pipelines to improve model performance and robustness. Analyze model performance and define validation methodologies and success metrics. Collaborate with quality and manufacturing experts to understand defect characteristics and improve model accuracy. Explore automated inspection workflows, including multi-view image acquisition and solutions that can evolve from an internal evaluation tool into a customer-facing quality assessment capability. Document experiments, datasets, models, and development processes, and present findings to engineering and business stakeholders. Requirements: Currently enrolled in a Master's degree in Computer Science, Artificial Intelligence, Data Science, Computer Vision, Robotics, or a related field, with at least 12 months of university enrollment remaining. Strong Python programming skills and solid understanding of machine learning, deep learning, and computer vision fundamentals. Experience with PyTorch and/or TensorFlow for model development and Tensorboard and/or MLFlow for experiment tracking. Familiarity with image classification, object detection, segmentation, and model evaluation methodologies. Experience with Git and collaborative software development practices. Preferred: Experience working with image datasets, data annotation, and dataset preparation. Preferred: Experience designing and building machine learning datasets from raw data, including image acquisition, data collection strategies, annotation guidelines, and quality control processes. Preferred: Experience with OpenCV, image processing, and image analysis techniques. Preferred: Experience with object detection and segmentation models such as YOLO, Faster R-CNN, DETR, U-Net, Mask R-CNN, or

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