[Hiring] Geospatial Full-Stack Engineer @Coulson Aviation

Role Description

This is a full-stack geospatial software engineering position on a small, early-stage product team. The near-term focus is pipeline and platform — ingesting thermal imagery from airborne sensors, extracting fire perimeters, and delivering georeferenced outputs to agency GIS systems. You will work closely with the founding Geospatial Platform Engineer to build and own that stack. As the product matures and data accumulates, the role evolves toward ML model development — a first-of-its-kind fire spread model that improves with every flight. The team is fully remote, and this position will require some travel for team events and occasional stakeholder meetings. You will be the first person in this role, and you will define what it looks like.

Areas of Ownership:

• Geospatial Platform Development — own the build-out of the ORBIT Data Engine — an end-to-end pipeline that ingests thermal imagery from airborne sensors, extracts fire perimeters, and publishes georeferenced outputs to agency GIS systems (AGOL, WildfireSA, NIFC).
• Front-End & Visualization — build and maintain the map-based interfaces and data visualizations that make ORBIT outputs accessible to agency users and flight crews.
• Data architecture — design the training data schema, feature engineering pipeline, and validation dataset strategy that makes the fire spread model improvable over time.
• ML Model Development — lead the development of a novel ML-based fire progression model that learns and improves as incident data accumulates — a trajectory that grows as the data foundation matures.
• Other EmberWorks products or programs – contribute ML or geospatial engineering capacity to other program tracks as assigned.

Qualifications

• Geospatial fluency — comfortable with raster and vector data pipelines; PostGIS, GDAL, cloud-native geospatial formats (GeoParquet, COG), and GIS publishing (AGOL, OGC services).
• Remote sensing experience — IR or multispectral imagery processing; radiometric data, thermal thresholding, or equivalent raster analysis in an applied context.
• ML curiosity and aptitude — you don’t need to have shipped a production ML model, but you need to understand the fundamentals and be genuinely motivated to build toward it.
• Cloud infrastructure comfort — GCP or AWS; experience building and operating data pipelines using managed services (Cloud Run, Pub/Sub, Cloud Storage, or equivalent).
• Communication — you can explain model tradeoffs and data quality issues to non-technical stakeholders without losing the details that matter.
• Self-direction — this role does not have a playbook; you will write it.

Requirements

• Fire domain familiarity — any exposure to fire behavior models (FARSITE, FlamMap, PHOENIX), fire weather data, or operational fire context; you don’t need to be an FBAN but you need to learn fast.
• Data assimilation or ensemble methods — familiarity with frameworks like WRF-SFIRE, EnKF-FARSITE, or similar; understanding of how observational data gets assimilated into running model states.
• Hardware-adjacent experience — familiarity with configuring or troubleshooting sensor hardware, working with raw sensor output, or integrating embedded systems into a data pipeline.
• Photogrammetry or structure-from-motion experience — familiarity with georeferencing, orthorectification, or photogrammetric workflows.
• Preference for west coast location.

Benefits

We support diversity, equity, and a workplace that is free from harassment and discrimination. We are committed to providing accommodation for people with disabilities. If you require accommodation through any element of the job application process, please notify us and we will work with you to meet your needs.