Senior Geospatial Machine Learning Engineer
New
J
JobgetherGeospatial AI
CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonMachine LearningPyTorchAirflowTensorflowDeep LearningComputer Vision
Requirements
- 5+ years of professional experience as a Machine Learning Engineer, Data Scientist, or similar role building and deploying production machine learning or deep learning models.
- Demonstrated experience developing computer vision or deep learning models using satellite or aerial imagery.
- Strong proficiency in Python and geospatial libraries such as rasterio, GeoPandas, Shapely, GDAL, or equivalent technologies.
- Hands-on experience with ML and deep learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Experience with data pipeline orchestration tools such as Dagster, Airflow, dbt, or comparable workflow management platforms.
- Familiarity with QGIS or equivalent tools for geospatial visualization and analysis.
- Experience with model monitoring, evaluation metrics, observability, and performance measurement in production environments.
- Strong analytical, debugging, communication, and problem-solving abilities, with the autonomy to lead projects in a distributed environment.
Responsibilities
- Develop and enhance vegetation intelligence products using geospatial Python libraries, machine learning, and deep learning techniques.
- Build, maintain, and optimize production ML models through data exploration, debugging, performance analysis, and iterative improvement.
- Work with satellite and aerial imagery to develop computer vision and deep learning solutions that address complex geospatial challenges.
- Lead projects end-to-end, from planning and technical execution through delivery, while clearly communicating project value and progress to cross-functional stakeholders.
- Create measurement frameworks, evaluation tooling, and performance metrics to assess model quality and guide data-driven priorities.
- Collaborate with upstream data ingestion teams and downstream product teams to shape scalable data pipelines, platform architecture, and product delivery.
- Monitor production systems and investigate issues using geospatial analysis, workflow orchestration, and observability tools.
- Contribute to continuous improvements in engineering practices, model reliability, and the effectiveness of geospatial intelligence solutions.
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