In this role, you will drive geospatial data and tooling needs for a large-scale project. You will be part of a team of GIS engineers responsible for sourcing geospatial data in many modalities, and for building geospatial data automation and tooling in an AI/ML pipeline for new applications. 

In this role, you’ll research and gather data for specific regions from different sources, including imagery and other raster data, vector data, and point clouds. You will find and acquire data from free/public domain sources, as well as form and drive partnerships with commercial data providers. You will be responsible for managing the coverage, freshness, quality, and cost of the sourced data. The most successful team members will have a working knowledge of disparate effective sources of data, and a curiosity for finding new ones. 

To prepare this data for AI/ML use, you’ll build and use automated tools (e.g., incorporating open source libraries like GDAL and in-house tools) to clean, resize, reformat, and align it so it’s ready for internal customers to use. You will also be responsible for tooling to automatically postprocess the output of a geospatial AI/ML pipeline for use by other teams. 

This role involves working closely with different teams in our organization. You’ll partner with the AI/ML teams to supply diverse data they need for building new machine learning models, and with the Pipeline and Product teams to ensure they have the necessary data required.  

Responsibilities include, but not limited to:  

• Aggregate data for target regions and ensure that a complete collection of data is available.  
• Research open, public domain, and commercial sensor data sources to determine the areas, quality, and freshness of available data.  
• Utilize open source (e.g. GDAL) and internally developed software tools to preprocess, align, crop, resample, and otherwise prepare data for use. We prefer open source tools over proprietary software, but understand that some software has no great open source equivalent. 
• Contribute to our internal tools that process and source geospatial datasets.  
• Partner with groups across the organization to understand needs for GIS data for AI/ML research. Coordinate with AI/ML team to provide diverse data for developing new machine learning algorithms, and the Pipeline and Product teams to provide data to roll out new regions.  
• Partner closely with leadership to understand the high-level product vision.