To support a growing ML Engineering team, the full-time ML Engineer will build and operationalize deep learning systems for estimating forest structure metrics from remotely sensed data while working remotely. Key Responsibilities Adapt and fine-tune geospatial foundation models for deep neural network applications that estimate forest structure metrics Integrate trained ML models into automated geospatial data pipelines and maintain data infrastructure Contribute to scientific manuscripts and serve as a liaison between SciDev and Data Engineering teams Required Qualifications M.S. in Computer Science, Machine Learning, Remote Sensing, Data Science, Ecology, or a related field (or equivalent experience) 3+ years of experience developing, training, and deploying deep learning models (PyTorch preferred) Strong proficiency in Python and data science libraries (NumPy, pandas, etc.) 3+ years of experience with geospatial data processing tools (rasterio, GDAL, etc.) Experience with data pipeline orchestration tools (Airflow, Prefect, or equivalent)