Deep Learning

2 weeks ago

Raanana Center District, Israel Rail Vision Ltd Full time ₪120,000 - ₪180,000 per year

Rail Vision is a multidisciplinary startup headquartered in Ra'anana. We are the leading provider of obstacle detection, collision avoidance and drive able areas 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 perception solution, Rail Vision systems detect obstacles on and along the tracks from up to 2 kilometers 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 analyzed data: Image-based Navigation, GIS Mapping and Predictive Maintenance.

We're looking for a brilliant Deep Learning & Computer Vision engineer to join our Algorithms group.

What will you do?

  • Be a researcher of the core deep learning algorithms and communicate with other researchers.
  • Design and implement advanced algorithms to solve complex problems.
  • Developing the next generation algorithms including different CNN architectures, CV algorithms and algorithms that utilize Physics in complex environment.

Requirements

  • Bachelor's or master's degree in computer science, Engineering, or a related technical field.
  • At least 3 years' experience as a researcher and development and deploying advanced algorithms.
  • In depth familiarity with deep-learning algorithms, classic machine learning and computer vision.
  • 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.
  • Deep knowledge in CNN for object detection, classification & segmentation

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