Deep Learning & Computer Vision Research Engineer

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.

#J-18808-Ljbffr