Senior Geospatial Backend Engineer

We are looking for someone who already does production geoprocessing in Python - rasterio, geopandas, numpy - and wants the geospatial layer to be the product rather than a chore buried under someone else's roadmap. M33 is building iris . You can ask a spatial question in plain language - "vegetation change along this corridor over the last three years" and iris composes a real, inspectable pipeline against satellite imagery and open vector data, which you can open, change and re-run. All orchestrated with trusted tools, with transparency and provenance about what’s happening all the way through. We are a small but growing team. You would be joining as our second engineer, and as another geospatial specialist - so we need someone who can function as a subject-matter expert as much as a developer. The engagement Independent contract, not employment. Remote, anywhere you can legally invoice as a business. Up to 40 hours a week at your own schedule, with at least 8 overlapping North American hours. US$45-55/hour , depending on depth and location. What you would own Our Python processing engine - the catalog of composable geoprocessing tasks iris builds pipelines from. Making a wrong answer hard to ship quietly. Nodata that survives masking, resampling and band maths. Reprojection that does not silently degrade. Pipelines that don’t run green if they’re returning a plausible but wrong number. Letting someone bring their own data into a pipeline, validating it, and refusing it loudly when it cannot be trusted. Cloud-native formats and access: COGs, STAC, GeoParquet, PostGIS, DuckDB spatial, dynamic tile serving. New data sources and connectors - satellite collections, global vector sources, disaster-event feeds. What we need to see Production experience with rasterio, geopandas and numpy. (This one is non-negotiable - substantial real work, not passing familiarity.) A spatial problem you took end-to-end into production. Any industry: agriculture, disaster response, urban planning, logistics, environmental monitoring. What matters is that you worked through the geoprocessing and shipped something that ran. Depth underneath those libraries - GDAL internals, coordinate systems and reprojection, raster and vector operations, STAC. Python you would be happy for someone else to maintain, and comfort working in Docker. The independence to set technical direction rather than wait to be told what to build. Nice to have: SAR or optical remote sensing, spectral indices, change detection, terrain and hydrology. Performance tuning of large raster reads. Knowing how you would prove a server does what it says it does. How to apply Use the link to access our (short!) application form. Tell us who you are, and one spatial problem you took into production. If you look like you might be a good fit for M33, we’ll reach out for an interview.