Machine Learning Scientist – Wildfire

Coulson Aviation

. Own EmberWorks' fire behavior modeling effort from first integration to new methods .

Posted 10/24/2026full-timeRemote • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in fire behavior modeling, including the development and validation of models using machine learning and empirical methods. Proficient in scientific geospatial programming and capable of managing cloud-based computing resources for model training and inference.

Highest-signal resume keywords

Fire Behavior ModelingMachine Learning Model DevelopmentScientific Geospatial ProgrammingRemote Sensing ExpertiseCloud Computing Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Fire Behavior ModelsMachine LearningData-Driven ModelsPython ProgrammingXarrayGDALRasterioPyTorchModel ValidationData Assimilation

Soft Skills

Independent WorkCommunication Skills

Tools & Technologies

GPU ResourcesCloud ComputeGit Version Control

Industry Keywords

Wildfire Project ManagementThermal Sensor CalibrationWildland Fire OperationsAcademic Fire Science CommunityRemote Sensing

Tech Stack

Tools & technologies

CloudPythonPyTorchRemote Sensing

About the role

Key responsibilities & impact

Own EmberWorks' fire behavior modeling effort from first integration to new methods
Evaluate existing fire behavior modeling approaches and select the right fit for available 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 using empirical, physical, and machine learning methods as appropriate
Own model validation and interpretation by defining quality criteria, detecting bad outputs, and determining whether issues originate in the model or upstream data
Define compute requirements for training and inference
Work with the platform engineer to establish GPU and distributed computing resources and an efficient remote workflow
Maintain connections with the academic fire science community
Help EmberWorks engage with research partners
Shape modeling approaches, validation methods, and how outputs reach users

Requirements

What you’ll need

Deep understanding of fire behavior models such as ELMFIRE, WRF-SITE, and FSIM, including underlying physics and math
Hands-on experience building and training machine learning or data-driven models on spatiotemporal data
Strong remote sensing background, including raster imagery; thermal experience is a strong plus
Proficiency in scientific geospatial programming stack, such as Python, xarray, GDAL, rasterio, and 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
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 a relevant field, or equivalent applied experience

Benefits

Comp & perks

Founding position on a small team doing new work with unique airborne fire observation data
Work tested on active fires rather than only benchmark datasets
Direct access to leadership
Room to set technical direction