This position involves developing, testing and deploying algorithms for autonomous flight. Specifically, using deep learning techniques for dynamic obstacles detection and terrain classification. The engineer will play a key role in a small, fast-moving team and have input to conceptual system architectural design and implementation of embedded software to ensure safety of an electric-powered, fly-by-wire aircraft.
• Design multi-sensor configurations for obstacle detection.
• Work closely with flight test team to collect large data sets.
• Post-Process massive, messy, possibly-incomplete data sets to create labelled training and testing datasets.
• Develop, train, test, and deploy obstacle avoidance algorithms using deep learning techniques.
• M.S. or PhD in aerospace engineering or computer science, or related field.
• 3+ years industry experience developing multi-sensor machine learning algorithms with real-world data.
• Proficient in C++, Python, and Matlab.
• Strong foundation of computer vision and deep learning techniques.
• Basic understanding of version control technologies.
• Strong work ethic, enthusiasm to learn, and a passion for autonomous, electric, passenger aircrafts.
• Experience developing parallel algorithms using OpenCL or CUDA.
• Experience with real-time tracking algorithm - classical or deep learning.
• Experience with deep learning frameworks such as Pytorch, Caffe, TensorFlow.
• Familiarity with deep learning approaches for object detection/segmentation such as Deformable-ConvNets, YOLO, Mask-RCNN.
• Developing systems for probabilistic sensor fusion and data association using cameras, lidar, radar, IMU, GPS, etc.
Engineer Internship Opportunities:
This position involves working closely with deep learning engineers to manage, post-process and label data sets used for deep learning. As the engineer gains experience, they will have opportunities to contribute to detection algorithms. The importance of this position should not be underestimated.
• Bachelors degree in aerospace engineering or computer science, or related field.
• Intermediate knowledge of Python.
• Strong understanding of data structure.
• Demonstrable experience of image processing projects