Staff Machine Learning Engineer - Wildfire

Jobgether; United States; Remote; USD 120,000 - 150,000; Full time

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

Overview

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Staff Machine Learning Engineer - Wildfire in the United States and Canada.

This role offers the chance to lead cutting-edge machine learning initiatives that reduce wildfire risk and protect communities. You will develop and scale advanced ML models that analyze satellite and environmental data to predict vegetation and fuel conditions. The position involves designing robust data pipelines, building reproducible experimentation frameworks, and translating scientific insights into production-ready ML systems. You will collaborate with data scientists, ML engineers, and domain experts while mentoring peers and guiding architectural decisions. Your work will have a direct impact on wildfire prevention, grid resilience, and climate risk mitigation across diverse geographies.

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 geospatial and temporal datasets
• Collaborate with wildfire science and product teams to define modeling goals, evaluation metrics, and real-world impact
• Build reproducible experimentation frameworks and evaluation workflows to ensure scientific rigor
• Scale models from research to production, prioritizing performance, reliability, and explainability
• Drive architectural decisions, tooling improvements, and process evolution for maintainable ML systems
• Mentor and provide technical leadership to other engineers, promoting best practices in modeling and deployment

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 applied to geospatial data
• Skilled in end-to-end ML systems design, including data ingestion, preprocessing, model training, deployment, and monitoring
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, XGBoost, or LightGBM, and data tools like Dask, Spark, or GeoPandas
• Familiarity with cloud ML platforms (e.g., GCP, Vertex AI) and large-scale distributed infrastructure
• Proven ability to collaborate across technical and scientific teams, communicate complex concepts, and lead architectural discussions
• Based in the United States or Canada

Nice-to-Haves

• Background in wildfire science, forestry, or environmental modeling
• Experience with physics-based models, active learning, or uncertainty quantification
• Knowledge of model interpretability and data provenance for environmental ML systems
• Experience with deep learning for weather or climate data
• Experience working in remote-first or globally distributed teams

Benefits

• Competitive salary and equity opportunities
• Flexible, remote-first work environment with autonomy and support for in-person collaboration as needed
• Educational and professional development budget
• Access to cutting-edge technology and tools for ML research and production
• Collaborative, mission-driven team culture emphasizing openness, respect, and diversity
• Opportunities to contribute directly to wildfire risk reduction and climate resilience