Computer Vision Internship (Summer 2022)

You will have the opportunity to work with world-class CV Scientists, Data Scientists, and Engineers to solve industry-scaling problems,  process terabytes of data, write intelligent algorithms, and play with computer vision and machine learning models. You will receive mentorship from Orbital Insight engineers to help grow your technical knowledge, access to the managers and leadership to grow your personal and leadership skills, and several networking opportunities to connect with various organizations inside the company. We strive to make your experience fun and rewarding. Our mission at Orbital Insight is to use geospatial data to understand what is happening on and to the Earth. A major part of that vision is using computer vision to look at petabytes of aerial  imagery -- one of the founding insights of the company is that the availability of imagery is rapidly outstripping the number of humans available to look at it. Hidden within that imagery at scale are the answers to myriad questions ranging from development rates in countries around the world, to agricultural productivity, to deployments of ground and air military assets, to mass migration and immigration, to climate change, to the state of economic cycles in countries around the world. We are leading the charge to use AI and Data Science at scale to make sense of this data and inform decision makers around the world.Using Computer Vision, we have counted over 3 billion cars across global roads and parking lots, analyzing commercial patterns as well as city growth and development. We have recently expanded our capabilities to include detecting trucks, identifying hundreds of classes of airplanes, different types of armoured vehicles, and changes in land use. Other projects include site monitoring near facilities or bases, tracking rates of deforestation, measuring poverty levels around the globe, monitoring construction rates of new roads and buildings, and ground-breaking work in areas such as using simulated imagery to augment human-labeled training imagery of rare or important objects and land types.