E-Mobility Engineer / Machine Learning Engineer - Intelligent Mobility System

Duration: 9 months – Extendable Job Duties & Responsibilities As a Machine Learning Engineer, you will contribute to state-of-the-art machine learning infrastructure and relevant software (e.g. supervised learning, reinforcement learning, data management, and evaluation at unparalleled scale). You will implement cutting-edge deep learning models accelerating model training performance for intelligent mobility system applications and tackling open problems together with researchers. 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. Address large scale challenges in the machine learning development cycle, especially around distributed training and inferencing environment in the cloud and data engineering. Manipulate high-volume, high-dimensionality, structured data from driving logs 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. Qualifications, Skills & exp required Bachelor's Degree in Computer Science/Electrical Engineering, Math, or related field. Bachelors with at least 3-4 years of experience; 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 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 not required - but recommended. Proficient in Python and Unix 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 numpy, scipy, pandas, sklearn etc and comfortable with development in Linux (Optional) Real-time traffic and autonomous vehicle simulation experience with e.g. Unity, CARLA etc. (Optimal) Implemented state-of-the-art models from research papers (share paper/repos if you can). (Optimal) Experience with Deep Reinforcement Learning, Multi-agent distributed reinforcement learning (Optional) Experience with computer vision and large-scale distributed training. (Optimal) Publication in robotics/ML/CV conference #J-18808-Ljbffr