ROLE
– Build, train, and evaluate geospatial AI/ML models for applications such as crop classification, forest/biomass estimation, water quality retrieval, invasive species and land cover mapping, and change detection, under the direction of senior team members.
– Prepare and preprocess hyperspectral, multispectral, and SAR datasets: co-registration, atmospheric/radiometric correction, glint and shadow masking, chip generation, and label wrangling.
– Fine-tune and benchmark existing geospatial and foundation models on new tasks and datasets.
– Contribute to embedding- and segmentation-based approaches for large-scale image search and object-centric retrieval, as part of our broader platform’s semantic search capabilities.
– Integrate multimodal data sources: hyperspectral, SAR, weather, and ground-truth/in-situ data, into modeling pipelines.
– Support transfer learning and domain adaptation efforts to extend models from well-labeled to label-scarce regions.
– Validate models rigorously against ground truth and known physical/spectral relationships, and help build out shared validation tooling and benchmarks used across the team.
– Write clean, well-documented, and reasonably efficient Python code, and contribute to shared libraries and pipelines used by the broader analytics team.
– Collaborate with data engineers, solutions scientists, and product managers to move models from notebook to production pipeline.
– Document methodology, maintain experiment tracking, and clearly communicate results to both technical and non-technical stakeholders.

REQUIREMENTS
– 0–3 years of experience applying machine learning to geospatial, remote sensing, or other scientific/spatial data.
– Solid Python programming skills and working knowledge of ML libraries such as PyTorch, TensorFlow, or scikit-learn.
– Foundational understanding of spectral data and willingness to build deeper hyperspectral expertise on the job.
– Familiarity with core geospatial data handling: raster/vector formats, coordinate reference systems, and tools such as Rasterio, xarray, GDAL, or PyProj.
– Understanding of fundamental ML concepts and willingness to learn domain-specific methods on the job.
– Comfortable working with large raster/imagery datasets and basic cloud or HPC compute environments.
– Strong communication skills and eagerness to learn from and collaborate closely with senior scientists.

BENEFITS
– Health insurance coverage
– Unlimited leaves & flexible working hours
– Role-based remote work and work-from-home benefit
– Relocation assistance
– Professional Mental Wellness services
– Employee Stock Options for all hires