About

• 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 opportunity to apply advanced machine learning to environmental and geospatial challenges, directly impacting wildfire prevention and public safety. You will lead the development and scaling of wildfire fuel detection models, working with large satellite and environmental datasets to predict vegetation risks. Collaborating closely with data scientists, ML engineers, and product teams, you will balance scientific rigor with production-ready systems, ensuring reliability, performance, and explainability. As a senior technical leader, you will mentor engineers, define architectural standards, and drive innovation across ML pipelines. This role combines environmental impact, cutting-edge technology, and leadership in a remote-first, collaborative team environment.
• Accountabilities
• Architect and implement advanced ML models for vegetation and wildfire fuel detection across diverse geographies.
• Design and maintain scalable data and feature pipelines for large-scale geospatial and temporal datasets.
• Collaborate with wildfire science and product teams to define modeling objectives, evaluation metrics, and success criteria.
• Build reproducible experimentation frameworks and model evaluation workflows to ensure accuracy and reliability.
• Scale ML models from research to production, optimizing for performance, reliability, and interpretability.
• Lead the evolution of ML systems, tooling, and processes to maintain state-of-the-art wildfire fuel modeling.
• Mentor engineers and contribute to architectural decisions, modeling standards, and best practices.
• Work closely with MLOps peers to streamline training, inference, and monitoring in production environments.
• 10+ years of experience designing and deploying production-grade ML pipelines and systems (strong candidates with 6+ years are considered).
• Deep learning, computer vision, or remote sensing expertise, with hands-on experience applying these methods to geospatial data.
• Proficiency in Python, PyTorch, TensorFlow, XGBoost, LightGBM, or similar frameworks.
• Experience with distributed data tools such as Dask, Spark, or GeoPandas.
• Familiarity with cloud-based ML platforms (GCP, Vertex AI, or similar).
• Strong understanding of end-to-end ML systems, from data ingestion to deployment and monitoring.
• Excellent collaboration and communication skills across technical and scientific teams.
• Ability to lead architectural discussions, define standards, and mentor other engineers.
• Based in the United States or Canada.

Nice-to-haves

• Background in wildfire science, forestry, or environmental modeling.
• Experience integrating physics-based models with ML, active learning, or uncertainty quantification.
• Knowledge of model interpretability, reproducibility, and data provenance for environmental ML systems.
• Experience with deep learning for climate, weather, or environmental data.
• Experience working in remote-first, globally distributed teams.
• Competitive salary and equity compensation.
• Flexible, remote-first work environment with autonomy over your schedule.
• Remote work budget, educational budget, and time to develop new skills.
• Opportunity to contribute to mission-driven work reducing wildfire risk and supporting environmental resilience.
• Collaborative, vibrant team culture emphasizing openness, respect, and support.
• Annual in-person team gathering and optional smaller in-person collaboration events.
• Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.
• When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.
• 🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
• 📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
• 🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
• 🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.
• The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company offering the position. The final decision and next steps (such as interviews or additional assessments) are handled by their internal hiring team.
• Thank you for your interest!
• #LI-CL1