Staff ML Engineer - Wildfire & Climate Tech (Remote)
Jobgether
United States
Remote
USD 120,000 - 150,000
Full time
14 days+
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Benefits offered by this job
Competitive salary and equity opportunities
Flexible, remote-first work environment
Educational and professional development budget
Access to cutting-edge technology
Collaborative team culture
Contribute to wildfire risk reduction
Job summary
A leading technology firm in the United States is seeking a Staff Machine Learning Engineer - Wildfire to spearhead cutting-edge ML projects aimed at reducing wildfire risk. This role involves developing and scaling advanced ML models from research to production while collaborating with data science teams. Ideal candidates will have significant experience in machine learning engineering with expertise in deep learning and geospatial data. Competitive salary and flexible working conditions offered.
Qualifications
6+ years of experience in machine learning engineering, with preference for 10+ years in production-grade ML systems.
Strong expertise in deep learning, computer vision, or remote sensing.
Responsibilities
Architect, develop, and maintain ML models for vegetation mapping and wildfire fuel detection.
Design and manage scalable data and feature pipelines for large-scale datasets.
Collaborate with teams to define modeling goals and evaluation metrics.
Skills
Deep learning
Computer vision
Remote sensing
ML systems design
Data ingestion
Model deployment
Collaboration
Tools
PyTorch
TensorFlow
XGBoost
LightGBM
Dask
Spark
GeoPandas
Job description
A leading technology firm in the United States is seeking a Staff Machine Learning Engineer - Wildfire to spearhead cutting-edge ML projects aimed at reducing wildfire risk. This role involves developing and scaling advanced ML models from research to production while collaborating with data science teams. Ideal candidates will have significant experience in machine learning engineering with expertise in deep learning and geospatial data. Competitive salary and flexible working conditions offered.