Matter Intelligence - Senior Geospatial Scientist
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Requirements
• PhD in a Geospatial-related field with 2+ years of experience, OR Master's degree with 5+ years of experience. • Proven experience processing hyperspectral reflectance data and developing associated algorithms. • Experience applying geospatial analysis in at least two distinct domains (e.g., Agriculture, Mineralogy, Ecology, Cryosphere, Aquatics). • Strong proficiency in Scientific Computing (Python, NumPy, SciPy, xarray, rasterio, GDAL). • Experience with AWS cloud infrastructure (S3, EC2, Lambda, Batch, or similar). • Deep understanding of math/statistics and geospatial fundamentals (coordinate projections, remote sensing instrumentation, GIS theory). • Demonstrated high proficiency in using LLMs (ChatGPT, Gemini, Claude) for coding productivity and workflow optimization. • Experience with spaceborne or airborne remote sensing missions. • Familiarity with atmospheric correction models (e.g., MODTRAN, 6S, FLAASH). • Experience with deep learning frameworks (PyTorch, TensorFlow) for geospatial applications. • Track record of publications or patents in remote sensing or geospatial science. • Experience in rapid development or startup environments. • This role is based in San Francisco, CA, with onsite presence required (temporary remote flexibility may be considered). Ability to travel to San Francisco Bay Area or El Segundo offices as needed. • To comply with U.S. export regulations, applicants must be one of the following: • A U.S. citizen or national • A lawful permanent resident (green card holder) • Eligible to obtain required authorizations from the U.S. Department of State • You are a scientist-engineer who bridges deep domain expertise with production-grade software. You think rigorously about data quality, communicate clearly with both technical and non-technical stakeholders, and thrive in environments where your work directly shapes products that matter.
Responsibilities
• Algorithm Development • Design and implement Machine Learning (ML) and physics-informed algorithms for hyperspectral data analysis. • Develop spectral unmixing, classification, and regression models for diverse geospatial applications. • Translate domain science (agriculture, mineralogy, ecology, aquatics) into validated algorithmic approaches. • Leverage modern AI tools to accelerate code generation and problem-solving. • Pipeline Architecture • Build and maintain scalable data processing pipelines on AWS to handle large-scale geospatial datasets. • Architect systems for radiometric correction, atmospheric compensation, and geometric orthorectification. • Optimize pipelines for throughput, latency, and cost across petabyte-scale imagery. • Scientific Validation • Evaluate the efficacy and accuracy of data products with rigorous statistical validation. • Maintain deep scientific understanding of all pipeline elements—from sensor physics to final output. • Design and execute calibration/validation campaigns using ground truth and reference data. • Team Collaboration • Partner with the Image Processing team to refine algorithms and support high-volume data processing. • Work closely with sensor engineers, systems engineers, and mission operations to ensure end-to-end data quality. • Contribute to technical proposals, publications, and customer engagements. • What Success Looks Like • Production-ready algorithms delivering validated data products at scale. • Robust, well-documented pipelines handling diverse hyperspectral workflows. • Clear scientific validation demonstrating data product accuracy and reliability. • Cross-functional partnerships enabling seamless sensor-to-insight workflows.
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