Deep learning research engineer
# Deep learning research engineer **Rail Vision** · רעננה, מחוז המרכז · `On-site` 🕒 **Статус:** *Опубликовано: вчера* · *Источник: Indeed* --- ### About the Role Rail Vision is a multidisciplinary company headquartered in Ra'anana. We are the leading provider of obstacle detection & classification systems in the railway industry around the globe, supporting Autonomous Train Operations. With our unique cognitive sensor fusion technology, based on advanced electro-optic sensors and deep learning, Rail Vision systems detect objects and obstacles on and along the tracks from up to 2 k”m away, in real-time, and in diverse weather and lighting conditions. We provide systems capable of operating in various environments: On mainline and high-speed rail, in urban environments, and in challenging switchyards. Rail Vision offers a range of complementary features based on collected and analysed data: Image-based Navigation, GIS Mapping and Predictive Maintenance. We’re looking for an experienced Deep Learning & Computer Vision research engineer to join our Algorithms group. What will you do: Be a researcher of the core deep learning algorithms and partnering with other teammates. Developing our next generation 2D/3D perception models that utilizes CV in complex environment. Work across full ML cycle: data, design, train and debug, solvIng complex problems that aligned to customer KPIs. Requirements: Master’s degree in computer science, Engineering, or a related technical field or very experienced Bachelor. At least 6 years’ experience as a researcher engineer with deploying advanced algorithms. Understand deeply, train, and evaluate recent state-of-the-art algorithms in computer vision (e.g., object detection, semantic segmentation, self-supervised learning, vision transformers) for various domains. Experience in Python & PyTorch or other machine learning frameworks and mathematical modelling. Good communication skills, self-motivated, proactive, flexible and passionate about learning Focus on end to end delivery of solutions for real world scenario in challenging timeframe.
Наблюдалась 2026-09-15, впервые 2026-09-14, источник — Indeed.