Mobileye

Algorithm Engineer in Car Sensing

Haifa, Israel
Python Deep Learning Machine Learning
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Description
The In Cabin Sensing group is intended to solve computer vision problems with a semantic aspect to understand the human driver in partially and fully autonomous driving scenarios. In our group, we research, develop, and deploy state-of-the-art technologies to achieve world-class results which enables the future development of autonomous driving features and advanced driver assistant systems.

The Driver Monitoring System is responsible for sensing and understanding the driver’s state and his relationship with the environment. The technology we develop combines state-of-the art deep neural networks and computer vision algorithms for real-time applications deployed in vehicles – contributing to saving lives and enabling autonomous vehicles.   

Check out our technology in action: https://www.youtube.com/watch?v=50NPqEla0CQ

What will your job look like:

  • You will be responsible for designing and developing algorithms for a real-time application based on computer vision algorithms.
  • You will lead end-to-end projects, from product requirements and characterization to algorithm design and implementation.
  • You will work closely with system architects, product managers and software engineers.
  • You will be part of a growing team, developing an innovative product inside Mobileye.
  • Your work will be part of Mobileye’s autonomous and driver assistance systems, you will be able test and demonstrate the system in real life scenarios.

All you need is:

  • BSc/MSc in Electrical engineering, computer science or equivalent degree from a leading university.
  • Background in Neural Networks/Deep Learning/computer vision- a must.
  • 3+ years of experience in computer vision and machine learning algorithm development.
  • Good programming skills in Python.
  • Experienced with system design for algorithmic pipelines. 
  • PhD- an advantage.
Mobileye
Mobileye
Artificial Intelligence Automotive Autonomous Vehicles Fleet Management Transportation

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