Machine Learning Wildfire Scientist

• 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.

• 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.

• 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.

• 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.

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.