Geospatial ML Engineer for Satellite Imagery

Vantor Holdings, Inc.; United States; Remote; USD 147,000 - 215,000; Full time

Benefits offered by this job

401(k) with company match

Mental health resources

Student loan repayment assistance

Adoption reimbursement

Pet insurance

Job summary

Vantor Holdings, Inc. is seeking a Machine Learning Engineer to turn satellite imagery into high-quality data and models. You will design scalable data pipelines and train models, collaborating with ML and software teams to deliver low-latency annotation systems.

You will ship reliable data services, deploy production pipelines, and continuously improve accuracy and traceability across datasets and annotations.

Qualifications

• 5+ years in ML engineering, data engineering, or related roles.
• Hands-on experience training or adapting deep learning models.
• Experience deploying ML models to production with inference pipelines.
• Experience with cloud services and IAM, storage, and databases.
• Experience in building data/ML workflow orchestration and CI/CD pipelines.
• Proven ability to support production systems with observability and incident response.
• Strong Python development skills and degrees in CS, ML, or related fields.

Responsibilities

• Build pipelines for large geospatial imagery and metadata.
• Develop, deploy, and evaluate ML models for proposed annotations.
• Operate production inference pipelines and track model performance.
• Ensure data lineage and versioning from source image to labels.

Skills

Python programming

Cloud platforms

CI/CD pipelines

Observability

Production deployment

Education

Bachelor's degree in Computer Science or related

Tools

Google Cloud Platform

Vertex AI

Kubeflow Pipelines

PostgreSQL

PostGIS

GDAL

Rasterio

Shapely

Fiona

GeoPandas

Job description

Vantor Holdings, Inc. is seeking a Machine Learning Engineer to turn satellite imagery into high-quality data and models. You will design scalable data pipelines and train models, collaborating with ML and software teams to deliver low-latency annotation systems.

You will ship reliable data services, deploy production pipelines, and continuously improve accuracy and traceability across datasets and annotations.