Experience in model interpretability and data provenance for environmental ML systems * Experience with deep learning models for weather or climate data * Experience in remote-first or globally ...
Experience in model interpretability and data provenance for environmental ML systems * Experience with deep learning models for weather or climate data * Experience in remote-first or globally ...
Mechanistic Interpretability information
What is the difference between Mechanistic Interpretability vs Data Scientist?
| Aspect | Mechanistic Interpretability | Data Scientist |
|---|---|---|
| Required credentials | Advanced degrees in AI, ML, or related fields | Degree in Data Science, Statistics, or Computer Science |
| Work environment | Research labs, AI development teams | Business, tech companies, consulting firms |
| Industry usage | AI research, model transparency, safety | Data analysis, predictive modeling, insights |
| Search intent | Understanding model internals, interpretability techniques | Data analysis, insights, model building |
Mechanistic Interpretability focuses on understanding how AI models work internally, often requiring deep technical expertise. Data Scientists analyze data to build models and extract insights. While both roles involve data and algorithms, Mechanistic Interpretability is more research-oriented, emphasizing transparency and safety of AI systems, whereas Data Scientists focus on practical data analysis and modeling for business applications.
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Job description
Role & Team
As a Staff Machine Learning Engineer at Overstory, you will lead the development and scaling of our Wildfire Fuel Detection Model. This core engine powers how we understand vegetation structure, fuel loads, and wildfire risk from satellite and environmental data. You'll help shape the next generation of Overstory's modeling capabilities by combining cutting-edge ML techniques, large-scale geospatial data, and real-world domain expertise.
Reporting to our VP of Product Engineering, you'll work closely with data scientists, ML engineers, and product teams to ensure our wildfire models are accurate, robust, and production-ready - balancing scientific rigor with practical engineering excellence. As a senior technical leader, you'll mentor other engineers, drive architectural decisions, and define standards for modeling, experimentation, and deployment across Overstory.
Time zone requirement: Eastern North America (NST, AST, EST)
What You'll DoIn collaboration with data, ML, and science colleagues, you will:
- Architect and build advanced ML models to map and predict vegetation and fuel conditions across diverse geographies.
- Design and maintain robust data and feature pipelines for large-scale geospatial and temporal data.
- Partner with wildfire science and product teams to define modeling objectives and evaluation metrics tied to real-world impact.
- Build reproducible experimentation frameworks and model evaluation workflows.
- Scale models from research to production with a focus on performance, reliability, and explainability.
- Lead the evolution of ML systems, tooling, and processes - ensuring that our wildfire fuelscape models remain state-of-the-art and maintainable.
- Collaborate with MLOps peers to streamline training, inference, and monitoring in production environments.
- Experience thriving at the intersection of machine learning, geospatial data, and environmental science; deeply motivated by the opportunity to reduce wildfire risk through data-driven insights
- 10+ years of experience designing and building production-grade ML pipelines and systems
- Strong background in deep learning, computer vision, or remote sensing
- Skilled in designing end-to-end ML systems - from data ingestion and preprocessing to deployment and monitoring
- Hands-on experience with frameworks like PyTorch, TensorFlow, XGBoost, or LightGBM, and data tools like Dask, Spark, or GeoPandas
- Familiarity with GCP and Vertex AI, or similar cloud-based ML platforms
- Strong communication skills and ability to collaborate across technical and scientific domains
- Comfortable leading architectural discussions and mentoring other engineers
- Background in wildfire science, forestry, or remote sensing
- Experience integrating physics-based models with ML or working with active learning and uncertainty quantification
- Experience in model interpretability and data provenance for environmental ML systems
- Experience with deep learning models for weather or climate data
- Experience in remote-first or globally distributed teams
Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.
What We Offer- Competitive, location-specific compensation and benefits
- Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
- Home office stipend, coworking and ongoing education budgets
- A company culture that genuinely embodies each of our core values
- To be part of truly mission-driven work that reduces wildfires, protects earth's natural resources and helps solve our climate crisis