Remote ML Engineer - Geospatial DL for Forest Risk

Vibrant Planet; United States; Remote; USD 100,000 - 200,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Unlimited PTO

Company equity

Home office stipend

Cell phone stipend

Job summary

Vibrant Planet is seeking an ML Engineer to build and operationalize foundation-model based systems that estimate forest structure metrics from remotely sensed data. You will fine-tune geospatial models, integrate them into automated pipelines, and maintain data infrastructure in a remote US role.

You will collaborate with SciDev, Data Engineering, and Product teams, contribute to manuscripts, and help ensure robust, scalable ML solutions in a cloud-based environment.

Qualifications

• MS or equivalent in a quantitative field such as CS, ML, remote sensing, data science, or ecology.
• 3+ years building, training, and deploying deep learning models (PyTorch preferred).
• Strong Python and data science stack experience; GIS data processing.
• Experience with data pipelines and orchestration tools; containerization.

Responsibilities

• Adapt and fine-tune geospatial foundation models for forest metrics.
• Integrate ML models into containerized, automated geospatial pipelines.
• Develop data ingestion, preprocessing, and quality control for satellite data.
• Collaborate across SciDev, Data Engineering, and Product teams.

Skills

Python proficiency

Git/GitHub

Airflow

Docker

Remote sensing

Geospatial data

Data pipelines

Education

M.S. in CS/ML/Remote Sensing/Data Science/ Ecology or related field

Tools

Docker

Airflow

PostGIS

AWS

GitHub

Job description

Vibrant Planet is seeking an ML Engineer to build and operationalize foundation-model based systems that estimate forest structure metrics from remotely sensed data. You will fine-tune geospatial models, integrate them into automated pipelines, and maintain data infrastructure in a remote US role.

You will collaborate with SciDev, Data Engineering, and Product teams, contribute to manuscripts, and help ensure robust, scalable ML solutions in a cloud-based environment.