ML Engineer
Builds and operationalizes deep learning systems that estimate forest structure metrics from satellite/lidar data, fine-tuning geospatial foundation models and integrating them into automated pipelines. Core stack: Python, PyTorch, Airflow, STAC, Docker, and AWS in a remote-first, climate-focused team.
- Compensation
- $100K–$200K / yr
- Location
- Remote USA
- Work setup
- Remote
- Seniority
- Lead
- Employment
- Full time
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- M.S. in Computer Science, Machine Learning, Remote Sensing, Data Science, Ecology, or related quantitative field (or equivalent work experience)
- 3+ years developing, training, and deploying deep learning models (PyTorch preferred)
- Strong Python proficiency including data science stack (NumPy, pandas, xarray, scikit-learn)
- 3+ years experience with geospatial data processing (rasterio, GDAL, geopandas, shapely)
- Experience building and maintaining data pipelines with workflow orchestration tools (Airflow, Prefect, Dagster, or equivalent)
- Proficiency with Git, GitHub, and collaborative software development practices (code review, CI/CD)
- Experience with containerization (Docker) and familiarity with cloud platforms (AWS preferred)
- Familiarity with STAC specifications and geospatial data catalog infrastructure
- Strong written communication skills; ability to contribute to scientific manuscripts and technical documentation
- Basic knowledge of forest ecology, remote sensing principles, or natural resource science
- Ability to work independently in a remote-first environment with strong organizational skills
Nice to have
- Ph.D. in a relevant field
- Experience with geospatial foundation models and self-supervised learning
- Experience with Kubernetes and distributed computing for large-scale inference
- Familiarity with ML experiment tracking (MLflow, W&B) and model registry practices
- Experience with database systems (PostgreSQL, PostGIS) and message queues
- Publications in remote sensing, ML, or ecology journals
Full description
ML EngineerPOSITION DETAILSDepartment: Engineering
Reports To: Engineering Manager, Infra Lead
Location: Remote, US | [Pacific / Mountain time zone preferred]
Employment: Full-time
ABOUT VIBRANT PLANET
We are a team of leaders in fire science, applied science, forestry, policy, and tech who work with land managers, community risk managers, utilities, and insurers to drive action that lowers the risk of destructive wildfire. Our cloud-based, AI-driven platform modernizes land management planning, community risk assessment, and monitoring through scenario building, decision support, and treatment outcome detection.
Our fire science subsidiary, Pyrologix, produces leading wildfire science and models that quantify wildfire hazard and risk, and the benefits of mitigation action. This science powers the core Vibrant Planet platform and supports our work across utilities, insurance, and other sectors.
Vibrant Planet is backed by climate and resilience leaders including Grantham Foundation, Earthshot, Elemental Excelerator, Ecosystem Integrity Fund, Cisco, and Halogen Ventures. For more information, visit and .
ABOUT THE ROLE
Vibrant Planet () harnesses data-driven science and cloud-based technology to help make communities and ecosystems more resilient in the face of global change. Our ML Engineering team sits at the intersection of machine learning, remote sensing, and forest ecology—building the models, pipelines, and data products that power our Land Tender decision-support platform.
We are seeking a ML Engineer to build, adapt, and operationalize foundation model-based deep learning systems that estimate forest structure metrics from remotely sensed data. In this role you will fine-tune and adapt geospatial foundation models as a backbone to custom deep neural network heads, integrate trained models into Vibrant Planet’s automated production pipeline, and maintain the surrounding data infrastructure. You will also contribute to scientific knowledge dissemination through manuscripts and serve as a key cross-team link between SciDev and Data Engineering.
KEY RESPONSIBILITIES
ML Model Development & Adaptation
- Adapt and fine-tune custom or publicly available geospatial foundation models as backbone architectures for domain-specific deep neural network heads that estimate forest structure metrics (canopy height, biomass, basal area, etc.).
- Prepare, curate, and manage training datasets from remote sensing sources (Sentinel-2, Sentinel-1, Landsat, lidar, NAIP) and field plot inventories.
- Evaluate model performance using standard remote sensing accuracy metrics and field-based validation data.