2 months ago
Remote, United States or Toronto, CanadaStaff+
Responsibilities
- Architect and build advanced ML models to map and predict vegetation and fuel conditions.
- Design and maintain robust data and feature pipelines for large-scale geospatial data.
- Partner with wildfire science and product teams to define modeling objectives.
- Build reproducible experimentation frameworks and model evaluation workflows.
- Scale models from research to production focusing on performance and reliability.
- Lead the evolution of ML systems and processes for wildfire fuelscape models.
- Collaborate with MLOps peers to streamline training and monitoring in production.
Requirements
- 10+ years of experience designing and building production-grade ML pipelines.
- Strong background in deep learning, computer vision, or remote sensing.
- Skilled in designing end-to-end ML systems from data ingestion to deployment.
- Hands-on experience with frameworks like PyTorch, TensorFlow, or XGBoost.
- Familiarity with GCP and Vertex AI or similar cloud-based ML platforms.
- Strong communication skills and ability to collaborate across domains.
- Comfortable leading architectural discussions and mentoring engineers.
Benefits
- Competitive, location-specific compensation and benefits.
- Flexible, autonomous, and collaborative working environment.
- Home office stipend, coworking, and ongoing education budgets.
- A company culture that embodies core values.
- Opportunity to be part of mission-driven work that addresses climate change.
Tech Stack
Categories
AI & MLData Science