We're working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve extreme-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity.
Come work on the Amazon Prime Air team!
As the lead image quality engineer, you will be part of the sensor imaging science team supporting all phases of the sensor development process. You will own the imaging quality and performance test lab and the development and maintenance of relevant software and hardware setup and tools. You will apply rigor in image quality and performance testing and generate analysis and reports to guide the imaging system development process. You will be detail oriented, hands-on and continuously push for high standards from yourself and the rest of the team. · Establish and maintain all image quality and system performance specifications and test procedures. · Identify image quality and performance issues. · Scope, schedule and organize, and perform testing in a timely manner and deliver robust test results to support development and evaluation. · Participate in new imaging components and systems design and development. · Support imaging systems and sensors manufacturing and deployment at scale.
· Master or Ph.D. degree in imaging science, optical science, sensing technologies, physics or equivalent · Have in-depth understanding of digital imaging systems and performance evaluation. · 5+ years of experience working on image quality evaluation and management for imaging sensors, digital cameras or other imaging systems. · Experience of designing and implementing image quality test protocols, performing data analysis and generating reports.
· Programming experience in Python, C++ or other equivalent programming languages are a plus. · Experience with imaging testing lab management and test setup design and implementation. · Experience in CMOS image sensor technologies and evaluation. · Experience in optical components performance testing and evaluation. · Experience in computer vision metrics and techniques. · Experience in computer vision metrics and methodologies. · Involvement in imaging science and image quality standardization efforts