Autonomous Vehicle Software Engineering Internship

1. Should have knowledge of Python, C++, deep learning (Keras or PyTorch), OpenCV, and Dlib

2. Should have knowledge of sensor fusion techniques like Kalman filters and its variations

3. Should have knowledge of localization concepts in robots, Markov localization, vehicle motion models, and particle filters

4. Should have knowledge of path planning, trajectory generation, and motion prediction of other vehicles

5. Should have knowledge about different controllers such as PID controllers and model predictive controllers

6. Should have knowledge of robot operating system (ROS), managing software packages, and writing ROS nodes

7. Should have familiarity with GPU-based training

8. Should know machine learning libraries (scikit-learn, SciPy, NumPy)

9. Should have familiarity with deep learning architectures such as convolutional neural networks [VGG16, ResNet, DenseNet], autoencoders, generative adversarial network, etc.

10. Having a self-driving car nano degree from Udacity or similar certifications is good to have

11. Having an understanding of 360-degree architecture for self-driving car software is an added advantage

12. Having familiarity with the Linux development environment and shell scripting is preferable

13. Having done projects related to self-driving cars or robotics or IoT is an advantage

14. Should be passionate about learning and implementing artificial intelligence

15. Having knowledge of JavaScript, mobile app development, and SQL is an added advantage