Deep Learning & Computer Vision Research Engineer
Pubblicato il 03-09-2026 - European Tech Recruit in Bressanone
Deep Learning & Computer Vision Research Engineer
European Tech Recruit are working closely with a computer vision & deep learning research center, based in Bressanone, who are looking for an talented Deep Learning & Computer Vision Research Engineer to join their team.
Their tech focuses on Industrial Multimodal AI: systems that combine images, video, 3D data, machine and sensor data, natural language, foundation models and generative AI to solve complex production and inspection challenges.
Responsibilities as Deep Learning & Computer Vision Research Engineer:
- Evaluate state-of-the-art research in computer vision, deep learning, multimodal AI and generative models.
- Translate industrial needs into research questions, datasets, benchmarks and proof-of-concept systems.
- Design and validate models for anomaly detection, segmentation, classification, object detection, visual retrieval and measurement.
- Adapt foundation and vision-language models to specialised industrial domains and limited-data scenarios.
- Develop data-efficient workflows using synthetic data, self-supervised learning, active learning and assisted annotation.
- Build reproducible pipelines covering data acquisition, training, validation, deployment and model monitoring.
- Deliver maintainable software, technical documentation and demonstrators, supporting technology transfer to industrial partners.
Requirements:
- MSc with at least three years of relevant experience, or a PhD in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Engineering or a related field.
- Professional proficiency in written and spoken English. Italian and/or German are advantageous
- Strong knowledge of modern computer vision and deep learning, including CNNs, Vision Transformers and foundation models.
- Ability to analyse research papers, reproduce results and translate new methods into working implementations.
- Strong Python and PyTorch skills, with familiarity with tools such as OpenCV, Open3D, PyTorch Lightning or Hugging Face.
- Experience in areas such as anomaly detection, segmentation, generative vision, multimodal learning, image retrieval or 3D vision.
- A data-centric approach to dataset collection, cleaning, annotation, versioning and quality assessment.
- Understanding of experimental design, benchmarking, reproducibility, model robustness and domain shift.
- Experience with GPU training, Linux, Git and containers; C++, MLOps, edge deployment and scientific publications are advantages.
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