Requirements:**Managers are seeking local candidates. They prefer for the candidates to work onsite in the Mountainview CA site for both positions**
- Master's Degree in Computer Science/Electrical Engineering, Math, or related field.
- 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.
- Familiar with containers
- Excellent verbal and written communication skills
- (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
As a Computer Vision 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. Last but not least, you will deploy your algorithms and models in our test vehicles platform. Responsibilities and required qualifications are as follows:
- 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.