Aerospace Machine Learning Group Lead at Aurora Flight Sciences
Cambridge, Massachusetts, United States
🇺🇸 (Posted Jun 20 2018)
About the company
Aurora Flight Sciences is a world leader in the development of highly autonomous aircraft. Our mission is to change the way we travel by applying autonomy and robotics to the development, production and operation of advanced aircraft.
Aurora Flight Sciences is a world leader in the development of highly autonomous aircraft. Our mission is to apply autonomy and robotics to the development, production, and operation of advanced aircraft. We aim to change the way we travel.
Aurora frequently achieves what others cannot due to a combination of experience, persistence, collaboration, and a culture of innovation. Projects are ambitious, risky, and fast paced. We apply breakthrough technologies to create a new state of the art, rather than advancing the old one. This is your chance to make history with us. We are seeking experts and explorers across the lifecycle of vehicle development. Cross-disciplinary interests are strongly desired; we love people that combine world-class expertise with a diverse background and interesting hobbies.
The Aerospace Machine Learning Group is part of the Autonomy Division at Aurora Flight Sciences’ Research and Development Center in Cambridge, MA. The Aerospace Machine Learning Group houses Aurora’s expertise in machine learning and data mining from supervised learning using reinforcement, active, and structured prediction to unsupervised learning; as well as, pushing the boundary in transfer learning and lifelong learning for use in cutting-edge autonomous aircraft and multi-vehicle systems.
The Aerospace Machine Learning Group Lead ensures the highest level of capability, consistency and quality in machine learning and data mining across Aurora. The Group Lead is responsible for maturing the company’s technical capabilities and methods (processes, tools, organization, etc.) in their area, and responsible for leading individual contributors to envision, develop, test, modify, and deliver practical solutions to technical problems.
Skills & requirements
Must be a US Person (US Citizen or US Permanent Resident/Green Card Holder).
Ph.D. in aerospace, mechanical, electrical, or computer science/engineering with a machine learning and data mining emphasis.
Five+ years of demonstrated relevant industry experience.
Demonstrated success in a highly collaborative environment with excellent English written and oral communications skills. The best measure of an effective manager is a high-performing team that consistently demonstrates esprit de corps. Strong interpersonal skills and an ability to positively motivate others are essential attributes.
Ability to apply (1) learning and acting under partial information (i.e. Predictive State Representation or POMDP) and (2) transfer learning whereby a realistic simulator is developed to enable deep learning for object classification that can be applied to real world scenarios. Focused expertise may be in either of these areas.
Ability to oversee setup, utilization, and maintenance of simulation and hardware-in-the-loop environments to develop, validate, and implement algorithms and software for real-time flight systems
Knowledge of state of the art in machine learning and data mining.
Experience at delivering products to numerous customers.
Experience in machine learning, artificial intelligence, pattern recognition, multi-robot collaborative control, and data mining.
Experience in image processing, embedded systems, ROS, OMS, or FACE open-systems architectures.
Experience with flight controls and flight dynamics of rotary and fixed-wing vehicles.
Strong C and C++ software development skills, especially in a Linux environment.
Industrial experience at performing software development in teams of 5+ developers, version control with TFS, git, or svn, and utilizes continuous integration.
Experience with test planning and execution.
Strong skills in Simulink and Stateflow, especially auto-coding.
Military and/or UAV experience.