Company

Cloud to Street is the world’s leading remote flood mapping platform. We use global satellites and remote sensing AI to monitor flood risk and detect worldwide floods in real-time. Seeded by Google, we have been used by governments across almost 20 countries for disaster relief efforts. Partnering with top insurers, we are now launching the first commercial parametric flood insurance product to better protect climate-vulnerable communities.

Role

We are looking for a Geospatial Data Engineer to help us scale up our deep learning efforts to turn optical and radar satellite imagery into actionable insights. In this role, you will take ownership of large projects in Cloud to Street’s Data Engineering agenda - building the full processing pipeline from training data collection to data labeling. You will work with a team of scientists and engineers with expertise in software engineering, remote sensing, machine learning and hydrology to turn petabytes of satellite data into meaningful information to empower the world’s most vulnerable communities.

This role is based in Brooklyn, NY and NYC area applicants are preferred. Remote work is possible within UTC -5 to UTC +1 time zones (Eastern Standard Time to Central European Time).

Who You Are

• Demonstrable experience with software automation of image processing and 1+ years remote sensing experience
  
• Coding proficiency, especially open-source geospatial Python packages, and programmatic handling of geospatial data formats (e.g. GeoTIFF, COG, Zarr, GeoJSON)
  
• Knowledge of how to scale code with cloud computing resources, e.g. GCP
  
• Git knowledge with high documentation and coding style standards
  
• Experience with geospatial data science (e.g. creating cartographic displays, data exploration, spatial statistics)
  
• Prioritize justice, diversity, science, and solidarity with vulnerable communities
  

Responsibilities

• Be the curator of the optical and radar satellite imagery datasets used for Cloud to Street’s algorithmic developments
  
• Take responsibility for the processing pipeline right up to the machine learning models. This includes creative planning to sample diverse flood events, coordinating and executing the collection, preprocessing, and analysis of these datasets
  
• Supervise the technical implementation of data labeling in cooperation with third parties and monitor their results, visualize annotations and make validated labels available for machine learning
  
• Work closely with our team of machine learning and radar scientists to translate rigorous science and research into robust products
  

To Apply

Send resume hiring@cloudtostreet.info with "Geospatial Data Engineer" in the subject line. Submissions should include an attached CV/resume and a paragraph of interest. Relevant past projects and/or publications are optional for submission.

Cloud to Street is devoted to building an inclusive and diverse company. Black, Indigenous, and people of color; women, queer people, and all gender identities, and individuals with disabilities are especially encouraged to apply.