Aurora Flight Science is a world leader in the development of highly autonomous aircraft.  We are seeking a dynamic individual who can aid in Aurora’s efforts to perform novel perception technologies on unmanned aircraft systems (UAS). 

The Perception and Sensing Group performs a key role in autonomy research at Aurora, developing hardware and computer vision software to architect vehicle autonomy solutions using vision-based localization and mapping algorithms.  The successful candidate will perform independent and team research and development involving architecting, coding, simulation, and integration of sensor systems to enable flight vehicle autonomy. 

Purpose of the role:

• Performing independent and team R&D involving integration of various types of sensor hardware.
• Implementing software for various perception algorithms for object detection/tracking and sensor data processing.
• Documenting and presenting research results to enable further growth of future commercial and government venture.

Required qualifications:

• BS in Computer Science, Aero/Astro, Electrical, Computer Engineering or other applicable field
• Experience with C/C++, Python, Linux and Git version-control system
• Experience in pattern recognition and machine learning, senor data processing and data fusion
• Generation and tracking of key performance indicators and regression testing
• Desire to work in a fast-paced environment
• Good communication skills
• Must be a US Person (citizen or permanent resident)

Preferred qualifications:

• MS or PhD in Computer Science, Aero/Astro, Electrical, Computer Engineering or other applicable fields with relevant work experience
• Theoretical and applied knowledge of radar signal processing, along with radar based perception algorithms
• Experience in Matlab and parallel programming on GPU using OpenCL or CUDA
• Experience in embedded hardware platform such as NVidia Tegra/Jetson or Intel NUC
• Experience in software architecture and Docker
• Experience in computer vision and 3-D point cloud segmentation