ML Engineer
Vibrant Planet · Remote · Remote USA ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- Fully remote (US)
- PyTorch + Python
- Geospatial AI focus
- Mid to Senior level
- No visa sponsorship
Join the ML Engineering team at Vibrant Planet, where machine learning intersects with remote sensing and forest ecology. You will develop models that estimate metrics like canopy height and biomass to power the Land Tender decision-support platform. The work supports land managers and utilities in lowering wildfire risk through data-driven science.
What you'll do
- Fine-tune geospatial foundation models for forest structure metric estimation
- Curate training datasets from Sentinel, Landsat, lidar, and field inventories
- Integrate trained models into automated geospatial data pipelines as containerized services
- Build and maintain STAC infrastructure for data discovery and access control
- Design idempotent and fault-tolerant pipelines using Airflow DAGs
- Monitor pipeline health and model drift, implementing retraining triggers
- Contribute to scientific manuscripts and technical documentation
- Collaborate with SciDev, Data Engineering, and Product teams
What you'll need
- M.S. in Computer Science, Remote Sensing, Ecology, or related field
- 3+ years developing and deploying deep learning models, preferably with PyTorch
- Strong Python proficiency with NumPy, pandas, xarray, and scikit-learn
- 3+ years experience with geospatial processing using rasterio, GDAL, and geopandas
- Experience building data pipelines with orchestration tools like Airflow or Prefect
- Proficiency with Git, GitHub, and CI/CD practices
- Experience with Docker and cloud platforms, preferably AWS
- Familiarity with STAC specifications and geospatial catalog infrastructure
- Basic knowledge of forest ecology or remote sensing principles
Nice to have
- Ph.D. in a relevant quantitative field
- Experience with geospatial foundation models and self-supervised learning
- Experience with Kubernetes and distributed computing for inference
- Familiarity with ML experiment tracking tools like MLflow or W&B
- Experience with PostgreSQL, PostGIS, and message queues
- Publications in remote sensing, ML, or ecology journals
Stack
- Python
- PyTorch
- NumPy
- pandas
- xarray
- scikit-learn
- rasterio
- GDAL
- geopandas
- shapely
- Airflow
- Docker
- AWS
- STAC
- PostgreSQL
- PostGIS
How you'll work
- Fully remote within the United States
- Pacific or Mountain time zone preferred
- Full-time employment
What you get
- Salary range $100,000 – $200,000
- Health, dental, and vision insurance
- 401(k) plan
- Unlimited PTO policy
- Company equity
- Cell phone stipend
- One-time home office setup allowance