Senior Geospatial Machine Learning Engineer
New
J
JobgetherClimate Technology
Romania, Collaborating across multiple time zones (Europe and the Americas)Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonMachine LearningPyTorchTensorflowDeep LearningComputer Vision
Requirements
- 5+ years of professional experience as a Machine Learning Engineer, Data Scientist, or closely related role, with experience building and deploying production models.
- Proven experience developing computer vision or deep learning models using satellite or aerial imagery.
- Strong proficiency in Python and geospatial Python libraries such as rasterio, geopandas, shapely, GDAL, or equivalent.
- Solid understanding of geospatial data structures, formats, processing workflows, and analysis techniques.
- Professional experience with ML and deep learning frameworks such as PyTorch, TensorFlow, scikit-learn, or comparable tools.
- Experience designing, implementing, or maintaining data pipelines using orchestration and workflow tools such as Dagster, Airflow, dbt, or equivalent systems.
- Experience with QGIS or comparable geospatial visualization and analysis software.
- Strong understanding of model evaluation, performance measurement, monitoring, and debugging in production environments.
- Ability to work effectively with large-scale, complex datasets and translate technical findings into practical product decisions.
- Strong project ownership skills to drive initiatives from planning through delivery.
- Excellent communication and collaboration skills in distributed environments.
Responsibilities
- Develop and deploy new vegetation intelligence products using machine learning, deep learning, computer vision, geospatial Python libraries, and large-scale satellite or aerial imagery.
- Explore geospatial datasets, identify opportunities for model improvement, optimize existing ML solutions, and troubleshoot production issues.
- Maintain and enhance existing vegetation modeling products to improve accuracy, reliability, scalability, and overall impact.
- Lead technical projects end-to-end, from defining objectives and planning implementation through execution, delivery, and evaluation.
- Develop measurement frameworks, evaluation tooling, and performance metrics that enable data-driven decisions about model quality and impact.
- Monitor production models and investigate performance issues using appropriate observability, monitoring, and debugging tools.
- Work closely with upstream data ingestion teams to influence data pipelines, processing workflows, and platform architecture.
- Partner with downstream product and delivery teams to ensure geospatial ML outputs can be effectively integrated into customer-facing solutions.
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