• Implement machine learning/ deep learning models (supervised learning, reinforcement learning) for the following tasks: 2D/3D object detection, semantic segmentation, depth estimation, traffic (time or speed) prediction and driving behavioral learning etc.
• Train and evaluate the developed machine learning model from large set of images/videos, build end-to-end training, testing, and evaluation pipelines.
• Manipulate high-volume, high-dimensionality, structured data from the vehicles for training and testing deep networks.
• Produce high quality tested code that enables large scale research and can be transferred to physical vehicles deployed in the real world.
• Stay up to date on the state-of-the-art in Deep Learning ideas and software, in collaboration with our Researchers.
• Coding, proof-of-concept (POC) and demo deep learning applications with our test vehicle platform.
• Work in a multidisciplinary team and collaborate with other teams across the research lab.

Required Skills:

• Masters with at least 1-2 years of experience; Strong software engineering practices in Python with machine learning experience in a production setting.
• Strong Machine Learning background with deep understanding of different types of machine learning algorithms (e.g., CNN, RNN, LSTM, Transformer, and Reinforcement Learning). Experience of training deep-learning models in an end-to-end fashion.
• Project experience working with Pytorch, Tensorflow or other modern deep learning frameworks.
• Computer Vision expertise.
• Proficient in Python and Linux is a minimum. Additional knowledge of C++ / CUDA is a plus, experience with AWS as well.
• Clear grasp on basic Linear Algebra, Optimization, Statistics, and Algorithms.
• Familiar with Python development co-system including opencv, numpy, scipy, pandas, sklearn etc and comfortable with development in Linux and Windows.