Project
A platform reshaping the vehicle inspection market, allowing companies to run guided, computer vision–based inspections on any smartphone in just a few minutes, with AI assessing damage and estimating repair costs.
Qualifications
- 3+ years of experience in developing and implementing deep learning models.
- Strong understanding of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Convolutional Recurrent Neural Networks (RCNN).
- Experience working with large datasets.
- Strong programming skills in Python, TensorFlow, and PyTorch.
- Strong AWS experience (deployment, optimization).
- Usage of open-source CNN models such as Yolo.
- Bachelor’s degree in Computer Science or a related field.
Responsibilities
- Design, develop, and implement deep learning models using CNN, RNN, and RCNN for various applications
- Work with large data sets to preprocess, clean, and prepare data for model training
- Create datasets for training and testing
- Train machine learning models and validate their accuracy, and deploy validated models into production
- Optimize models for performance and accuracy, and fine-tune hyperparameters to achieve optimal results
- Stay up to date with the latest developments in deep learning techniques and technology
- Provide technical guidance and support to other team members
