Merging artificial intelligence with computer vision, client's systems detect and react to what's happening on the road ahead of a driver and within the vehicle. The algorithms sense when there is an issue on the road ahead, or a distraction within the vehicle, and helps the driver respond. They also automatically understand when a collision is about to happen, and record the scene inside and outside of the car then. Images and data about the incident are stored in the cloud, and can be shared via client's app and fleet management tools.
The company's systems are already running on several classes of high-volume commercial vehicles. Longer term, client will continue to partner with auto manufacturers to evolve its systems from an onboard safety AI into a platform that accelerates the development of autonomous vehicles.
• Keeping up with recent AI research results and implementing/improving on winning DNN algorithms
Must• M.Sc. or Ph.D. in Computer Science, Math, Physics or similarly quantitative-heavy background
• Experience with implementing DNNs for computer vision problems such as object classification, object detection and localization, semantic segmentation
• Experience with deep learning frameworks such as Caffe, Theano, Torch, Keras or Tensorflow
• Familiarity with OpenCV or other computer vision libraries
• Experience with traditional computer vision methods is a bonus
• Knowledge of common Machine Learning methods
• Proficiency in C/C++, Python, Unix/Linux scripting
Nice to haveExperience in automotive projects
- English: Upper-intermediate