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Vantor

AI-powered multi-source geospatial intelligence platform

Machine Learning Engineer

Computer VisionUpdated on Nov 11
Full-timeRemote in USA

About the Job

Requirements

  • At least 5 years of relevant experience in machine learning engineering, data engineering, backend software engineering, or a closely related technical role.
  • Hands-on experience training, fine-tuning, or adapting deep learning models using a modern machine-learning framework.
  • Experience deploying machine-learning models into production, including building batch or real-time inference pipelines and managing model versions.
  • Hands-on experience building and operating services on a major cloud platform, including managed compute, databases, storage, and identity and access management.
  • Experience building and operating workflow orchestration for data or machine-learning pipelines.
  • Experience owning containerized services, continuous integration and continuous delivery pipelines, and infrastructure as code.
  • Ability to support production systems, including debugging, observability, secure configuration, and incident response.
  • Strong Python application development skills.
  • Bachelor's degree in Computer Science, Machine Learning, Data Engineering, Geospatial Science or GIS, Remote Sensing, or a related discipline, or equivalent demonstrated experience.

Responsibilities

  • Build systems that turn raw satellite imagery into trusted, well-labeled data and develop machine-learning algorithms to turn that data into models.
  • Work with internal domain experts, external annotation partners, and Machine Learning and Software Engineering teams to design robust, scalable, low-latency solutions.
  • Build pipelines for large geospatial imagery and metadata, including sensor, acquisition, and geolocation attributes, and move data between annotation, storage, and model-training systems.
  • Track data lineage and versioning so each label can be traced to its source image, guideline version, and annotator.
  • Develop, adapt, and deploy models that generate proposed annotations to reduce manual labeling effort and turnaround time.
  • Build and operate production inference pipelines, evaluate model performance against labeled data, and use model feedback to determine which imagery should be labeled next.

Desired Qualifications

  • Experience with Google Cloud Platform services, including Cloud Run, Cloud SQL, Cloud Storage, IAM, and service account management.
  • Experience with Vertex AI, including Vertex AI Pipelines, training jobs, and model deployment, or similar tools such as Kubeflow Pipelines.
  • Experience with PostgreSQL and PostGIS, including spatial data modeling and managing schema migrations in production.
  • Experience working with satellite or aerial imagery, including raster formats such as GeoTIFF, coordinate reference systems, and tools such as GDAL, Rasterio, Shapely, Fiona, and GeoPandas.
  • Experience developing computer-vision models for object detection, semantic or instance segmentation, or change detection, ideally applied to overhead imagery.

About the Company

Vantor

AI-powered multi-source geospatial intelligence platform

Vantor provides an AI-powered spatial intelligence platform that creates a real-time, unified picture from space, air, and ground sensors. Its Tensorglobe data engine fuses satellite imagery, drone feeds, and ground sensors to build a living 3D digital Earth and automates the entire intelligence cycle from tasking to analysis. Key products include WorldView for high-resolution imagery tasking, Raptor for GPS-denied navigation, and Sentry for automated site monitoring. Unlike competitors that mainly sell raw imagery, Vantor offers an integrated, subscription-based geospatial workflow and digital twin as a service, helping defense, intelligence, and commercial customers make faster, more informed decisions through a shared AI-ready view of the world.

Company size
1,001-5,000
Company stage
IPO
Headquarters
San Juan, Puerto Rico
Founded
1957

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