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