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# Machine Learning Wildfire Scientist

- **Company:** Coulson Aviation
- **Location:** Thermal, CA, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Applicant Tracking Systems, Cloud Computing, Computer Programming, Distributed Systems, Python (Programming Language), Machine Learning, Pytorch, Model Validation, Data Assimilation, Git, Information Technology, Geospatial Data Abstraction Library (GDAL), Data Analytics, Software Version Control, Data Pipelines, Raster Graphics
- **Published:** October 23, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=60d894b21b163cdf

## About the Role

* Deep understanding of fire behavior models (e.g. ELMFIRE, WRF-SITE, FSIM, etc), including the underlying physics and math, not just experience running existing models.
 * Hands-on experience building and training machine learning or data-driven models on spatiotemporal data.
 * Strong remote sensing background, including working with raster imagery (thermal experience a strong plus).
 * Proficiency in scientific geospatial programming stack (for example, python xarray, GDAL, rasterio, PyTorch or similar). Familiarity with Git based version control.
 * Experience running model training on cloud compute, including GPU resources.
 * Ability to work independently in a small R&D team and explain modeling decisions to engineers and operations staff.
 * Bachelor's degree in fire science, atmospheric science, remote sensing, computer science, environmental science or a related field.

Preferred

 * Proven experience delivering at least one wildfire project end to end, or sustained, in-depth support of an established wildfire product suite. Active ties to academic or government fire research groups.
 * Experience with data assimilation or physics-informed ML.
 * Knowledge of optics and radiometry, including thermal sensor calibration.
 * Familiarity with wildland fire operations and the agencies and tools that support them.
 * Advanced degree and research in fire science, atmospheric science, remote sensing, computer science, environmental science or a related field, or equivalent applied experience.

## Description

Operational fire behavior models have been built and calibrated on limited observations, and their predictions often diverge from what crews see on the ground. EmberWorks is positioned to help close that gap: our aircraft already fly over active fires as part of routine operations, collecting thermal observations as they go.

You will own EmberWorks' fire behavior modeling effort, from first integration to new methods. This is a new function for us and a founding position, so you will shape which approaches we pursue, how results are validated, and how outputs reach the people who use them.

You will join a small, hands-on team of geospatial and platform engineers who build our data pipeline and cloud infrastructure, and who will work closely with you to get the right data to your models. You will be the team's modeling expert, and we expect you to tell us what we're getting wrong.

What You'll Do

 * Evaluate existing fire behavior modeling approaches and select the right fit for our data.
 * Build and implement models in an automated production pipeline using airborne thermal observations plus external fuel, weather, and terrain data.
 * Research and develop new modeling approaches, drawing on empirical, physical, and machine learning methods as appropriate.
 * Own model validation and interpretation: define what "good" looks like, detect bad outputs, and determine whether a problem lies in the model or upstream in the data.
 * Define compute requirements for training and inference and work with our platform engineer to set up GPU and distributed computing resources and an efficient remote workflow.
 * Maintain connections with the academic fire science community and help EmberWorks engage with research partners.