Deep Learning Engineer Internship
Founded in 2015, Acubed is the Silicon Valley innovation center of Airbus. As a global leader in aerospace, Airbus aims to make things fly. Our mission is to provide a lens into the future for the industry, transforming risk into opportunity to build the future of flight now.
At Acubed, we strive to propel innovation to market faster, broaden the talent pool in emerging aerospace careers and simultaneously help drive a culture change across Airbus.
Project Wayfinder is building scalable, certifiable autonomy systems to power self-piloted aircraft applications throughout Airbus, from small urban air vehicles (aka air taxis) to large commercial airplanes. Our team of experts is driving the maturation of machine learning and other core technologies for autonomous flight; we are creating a reference architecture that includes hardware, software, and a data-driven development process to allow aircraft to perceive and react to their environment. Autonomous flight is transforming the transportation industry - our team is at the heart of this revolution.
As an intern at Wayfinder, you will work on meaningful engineering systems, not toy problems. You will contribute to scalable, robust, creative solutions alongside a small team of multidisciplinary engineers with a wealth of experience in aerospace, automotive, deep learning, and software.
This is your chance to build autonomous airplane systems! During the internship you will join our deep learning team, developing data acquisition and processing pipelines that will be deployed on our aircraft. The internship will be hands-on, allowing you to test the code you will write on a real system.
- Understand, modify, debug, and improve our neural network architectures, which are used for object detection, localization / regression, etc.
- Tune hyperparameters and run experiments to optimize the performance of our neural network architectures
- Develop proof-of-concept architectures based on the current state-of-the-art in deep learning research to evaluate whether new approaches might be applicable to our use cases
- Significant academic background in deep learning, computer science, computational neuroscience, or similar fields
- Working proficiency in PyTorch and/or TensorFlow
- A deep understanding of the principles of convolutional neural networks, as well as the state of the art in object detection
- Working proficiency in C/C++ or Python on a Linux-based OS
- Command of basic software engineering (e.g. iteration, data structures, object oriented programming) and best practices
- Intellectual curiosity to do what hasn’t been done before, coupled with a drive to overcome challenges
Strongly Preferred Qualifications
- Familiarity with recurrent neural networks, LSTMs, or other deep learning architectures for various domains
- Traditional computer vision algorithms (e.g. OpenCV)
- Real-world experience with autonomous systems (e.g. mobile robots, cars, aircraft) and related sensors (e.g. lidar, radar, cameras, inertial measurement units)
- Packaging and deployment of neural network models in production
- Aviation and piloting experience
- A passion for making things fly