Machine Learning Engineer

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what’s happening now and shape what’s coming next. Vantor is a place for problem solvers, changemakers, and go-getters—where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can:
Shape your own future, build the next big thing, and change the world.

To be eligible for this position, you must be a U.S. Person, defined as a U.S. citizen, permanent resident, Asylee, or Refugee.

Export Control/ITAR:
Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3).Please review the job details below.

Role Overview Vantor is seeking a Machine Learning Engineer to build the systems that turn raw satellite imagery into trusted, well-labeled data, and to develop machine learning algorithms to turn that data into models. Your work sets the quality, speed, and traceability of the datasets behind Vantor's geospatial and machine learning products. You will work closely with internal domain experts, external annotation partners, and Vantor's Machine Learning and Software Engineering teams to design robust, scalable, low-latency solutions.

On the platform side, you will build pipelines for large geospatial imagery and metadata, including sensor, acquisition, and geolocation attributes, and move data reliably between annotation, storage, and model training systems. You will also track data lineage and versioning so every label can be traced back to its source image, guideline version, and annotator.

On the machine learning side, you will develop, adapt, and deploy models that generate proposed annotations, cutting manual labeling effort and turnaround time. You will build and operate production inference pipelines, evaluate model performance against labeled data, and use model feedback to decide which imagery should be labeled next. This is a hands-on role for an engineer who is as comfortable shipping a reliable data service as training and deploying a computer vision model, and who can connect the two into a single, continuously improving system.

Minimum Requirements 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 ML 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 ML pipelines.

Experience owning containerized services, CI/CD pipelines, and infrastructure-as-code.

Proven 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.

Preferred 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 with similar tools such as Kubeflow Pipelines.

Experience with PostgreSQL and PostGIS, including spatial data modeling and managing schema migrations…