Aurora Flight Sciences, A Boeing Company, a world leader in the development of highly autonomous aircraft is seeking a talented and self-motivated individual who can aid Aurora's efforts to create the next generation of Artificial Intelligence / Machine Learning (AI/ML) technology for unmanned aircraft systems (UAS), aerospace and defense.
The candidate will develop, implement, and apply state-of-the-art AI/ML techniques to challenging problems in the aviation domain, including
- Implement AI/ML algorithms relevant to autonomous UAS in areas of control, optimization, navigation, state estimation, sensing, planning, prediction, and swarm operations.
- Develop and apply AI/ML techniques to diverse problems, such as, data analytics, situational awareness, computer vision, robotics, and natural language processing.
- Enhance and maintain current analytic tools, including automation of current processes using AI/ML techniques.
- Develop and prototype AI/ML software in both large distributed systems and embedded hardware platforms.
- Explore and propose novel AI/ML approaches in areas such as safe, trustworthy, explainable AI/ML methods, and human-machine teaming.
- Lead and participate in the development of research proposals.
- PhD in Computer Science, Aero/Astro, Mathematics, Statistics, Electrical Engineering, or related engineering fields. A Master's degree with at least 5 years of experience will also be considered.
- Experience with one or more of the following: Deep Learning, Reinforcement Learning, Bayesian Methods, Statistical Learning Theory, and Optimization Algorithms.
- Strong software programming skills and experience with Python and/or C++ programing.
- Experience with machine learning frameworks and open-source tools, such as, PyTorch, TensorFlow, MxNet, Scikit-Learn, Dlib, OpenAI Gym, etc.
- Formal coursework in Artificial Intelligence and Machine Learning.
- Ability to work in a multi-disciplinary team environment with effective oral/written communication ability
- Must be US citizen or US permanent resident.
- Experience with state-of-the-art AI/ML techniques, such as, Graph Neural Networks, Generative Adversarial Networks, Few-Shot Learning, Multi-Agent Reinforcement Learning, and Bayesian Optimization.
- Experience with AI/ML techniques for computer vision, point cloud processing, and/or robotics.
- Experience with all phases of AI/ML development lifecycle, from problem statement, data processing, model training, evaluation, and deployment.
- Experience with distributed systems and cloud computing.
- Experience with agile development methodologies and tools.
- Experience in proposal writing and program technical leadership is a big plus.