You will own the fire behavior modeling effort, from initial integration to developing new methods for analyzing airborne thermal data. You will collaborate with a team of engineers to build automated production pipelines and define compute requirements for model training and inference.
About EmberWorks
EmberWorks Sensing is the remote sensing arm of the R&D division of Coulson Aviation. Our flagship product, ORBIT, is a persistent aerial thermal sensing system flown on Coulson air attack aircraft. ORBIT automatically captures and delivers near-real-time fire intelligence straight into the tools incident teams already use.
The Role - Remote Job
Operational fire behavior modelshave 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.
What We're Looking For
Required
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.
How we Hire: -
We actually read your resumes and don't rely on automated filtering methods
We don’t care about ATS optimized resumes.
There is no gating system for which ones we receive.
We value honesty and authenticity.
If you submit a cover letter, we will read it.
Why Join
This is a founding position on a small team doing new work with unique airborne fire observation data. Your work gets tested on active fires, not just benchmark datasets, so you see quickly how it holds up in the field. You will also have direct access to leadership and room to set technical direction. We are looking for someone who finds that more compelling than a well-defined role at an established organization.
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