Machine Learning Scientist – Wildfire
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Job Description
Machine Learning Wildfire Scientist owning fire behavior modeling for EmberWorks Sensing's ORBIT airborne thermal sensing platform. Building validated production models from wildfire observations and geospatial data.
Responsibilities:
- 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:
- 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:
- 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
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