Analyzes aerial imagery to find actionable information.
Be in the middle of everything … and the only one who actually knows how it all works.
You’re in the core, the middle, the center. All roads go via you. Whatever it is they want, it requires you. You have deep experience using Python and varied open source packages to build serious cloud computing pipelines. We ingest many kinds of geospatial data and then we dice, slice, stretch, rotate, and combine them. We use deep learning to turn the pixels into numbers. We model, aggregate, transform the numbers into meaningful insights. This entire pipeline is formulated, created, monitored, and managed by you. Experience working at scale is pretty darned important.
The world is a very big place. With a single photograph of Earth, like the famous “Blue Marble” from Apollo 17, the entire world can fit on a computer screen. However, with this image you would not be able to see much detail. To see the Earth and all of its rich features, you would need a lot more images. In fact it would take over 500 million images just to observe the land area of Earth at 1 meter pixel resolution.
In order to see detail and work at scale, we use computer vision and machine learning. Using convolutional neural networks, we have counted over 3 billion cars across global roads and parking lots, analyzing commercial patterns as well as city growth and development. Our global energy product uses computer vision to map floating roof oil tanks and measure the global volumes of crude oil. With this product, we were able to show that China has 200 million barrels of oil storage capacity that was unknown even to the trading community. Other projects include tracking rates of deforestation, measuring poverty levels around the globe, monitoring construction rates of new roads and buildings, and predicting crop yields. This is still the beginning of our mission to understand the Earth.
This position will be based in our Mountain View, CA office.
Define algorithms for image ingestion, re-projection, tiling, normalization, spatial intersection, and other initial processing
Maintain and process large amounts of geospatial data; write code for automating complex geospatial requests
Build and maintain production pipelines for ingesting images from satellite/UAV operators and processing them in the cloud at scale
Work with machine vision and data science teams to define and implement algorithms for multispectral image analysis, machine vision, and data analysis
Work with front end team to serve the results up to our users
Skills & Qualifications
Bachelor’s Degree in Computer Science or related discipline
3+ years experience with Python or other similar language
Knowledge of GDAL or Postgis a major plus
Experience with large-scale analysis of satellite imagery a major plus
We are backed by marquee investors such as Sequoia Capital, Google Ventures, In-Q-Tel, and Bloomberg Beta, Orbital Insight is rapidly expanding its commercial and public sector capabilities. Come join us if you are unafraid to try new approaches and relentlessly seek to push the state of the art, taking projects all the way from prototype to production-ready.
– Stock Options
– Competitive Salaries
– Paid time off
– Medical, dental, and vision insurance
– Life & disability coverage
– Flexible Spending Accounts
– Apple equipment
– Twice a week catered lunches
– Monthly happy hour in office
– Stocked kitchen with healthy snacks, coffee, tea, etc.
– Easy commute via Caltrain, with company sponsored Go Pass
Note to Recruiters and Placement Agencies: Orbital Insight does not accept unsolicited agency resumes. Please do not forward resumes to any person or email address at Orbital Insight prior to obtaining a signed agreement from Recruiting/HR. Orbital Insight is not liable for and will not pay placement fees for candidates submitted by any agency other than its approved recruitment partners. Furthermore, any resumes sent without an agreement in place will be considered your company’s gift to Orbital Insight and may be forwarded to our recruiters for their attention.