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Mid-level ML role in a metro with a common Data Scientist title but niche geospatial focus.
Strong geospatial and computer-vision specialization reduces cross-industry transferability, raising sensitivity.
Explicit 3+ years requirement plus mandatory PyTorch, production ML, and distributed training/MLOps makes filters strict.
Develop and ship computer vision models (semantic segmentation, change detection, super-resolution, temporal sequence modeling) for geospatial problems using satellite data.
Own end-to-end ML project lifecycle: problem scoping, data pipeline design, model development, evaluation, deployment, and monitoring at planetary scale.
Write production-grade Python/PyTorch code; work with distributed training infrastructure and large-scale data pipelines across geographies and sensor modalities.
3+ years applied ML/Computer Vision experience or PhD plus 1+ year post-degree experience in CS, EE, or related field.
Strong fundamentals in semantic/instance segmentation, object detection, encoder-decoder and transformer architectures.
Proven experience taking ML models from prototype to production.
Proficiency in Python and PyTorch; familiarity with distributed training and MLOps tooling.
Experienced in computer vision at scale with ability to solve complex geospatial problems using multi-sensor satellite imagery.
Skilled at rigorous experimental design, metric definition aligned with business outcomes, and cross-team collaboration (product, MLOps, platform, geospatial).
Comfortable working on challenging, large-scale ML systems requiring clean production code and integration with distributed infrastructure.