Machine Learning Engineer

Vantor

. Build systems that turn raw satellite imagery into trusted, well-labeled data .

Posted 10/8/2026full-timeRemote • United StatesMid-LevelSenior💰 $128,000 - $215,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying machine learning models, particularly in the context of geospatial data and satellite imagery. Proficient in building production inference pipelines and managing data workflows while ensuring system reliability and performance.

Highest-signal resume keywords

Machine Learning EngineeringDeep Learning Model TrainingCloud Platform ServicesPython Application DevelopmentGeospatial Data Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning AlgorithmsModel DeploymentData EngineeringWorkflow OrchestrationContainerized ServicesCI/CD PipelinesPostgreSQLPostGISComputer Vision ModelsGeospatial Data Modeling

Soft Skills

CollaborationProblem-SolvingDebuggingIncident Response

Tools & Technologies

Google Cloud PlatformVertex AIKubeflow PipelinesGDALRasterioShapelyFionaGeoPandas

Industry Keywords

Satellite ImageryAerial ImageryRaster FormatsCoordinate Reference SystemsData LineageModel Versioning

Tech Stack

Tools & technologies

CloudGoogle Cloud PlatformPostGISPostgresPythonRemote SensingSQL

About the role

Key responsibilities & impact

Build systems that turn raw satellite imagery into trusted, well-labeled data
Develop machine learning algorithms and models from geospatial datasets
Build pipelines for large geospatial imagery and metadata, including sensor, acquisition, and geolocation attributes
Move data reliably between annotation, storage, and model training systems
Track data lineage and versioning so labels can be traced to source imagery, guideline versions, and annotators
Develop, adapt, and deploy models that generate proposed annotations
Build and operate production inference pipelines
Evaluate model performance against labeled data
Use model feedback to determine which imagery should be labeled next
Collaborate with internal domain experts, external annotation partners, and Machine Learning and Software Engineering teams
Support production systems through debugging, observability, secure configuration, and incident response

Requirements

What you’ll need

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
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 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

Benefits

Comp & perks

Robust 401(k) with company match
Mental health resources
Student loan repayment assistance
Adoption reimbursement
Pet insurance
Incentive eligible with a target based on contribution, company performance, and/or individual results achieved