Junior Geospatial Data Scientist (Pipeline & Algorithm Focus)

About this role

About Matter Intelligence

Welcome to Matter, where we are building the future of vision AI: pairing a world-first sensor that sees molecular chemistry, temperature, and 3D shape with a Large World Model that will be the most powerful intelligence engine for our physical world. This system doesn't just see what something looks like; it understands everything from a single pixel. We call this Superintelligent Vision.

You'll join a team that has delivered technologies to Mars for NASA/JPL, co-founded and led infrastructure for OpenAI, designed cutting-edge sensors for U.S. Defense, and invented core algorithms for spectral and 3D imaging. We've come together to build the next infrastructure for vision and intelligence in the physical world.

About the Role

We are looking for a Junior Geospatial Data Scientist to build and maintain the processing pipelines and algorithms that turn our satellite's raw hyperspectral imagery into science-grade data products. This is not a pure research role—you will write production code that runs at scale, and you'll need the math and physics intuition to know
*why*
each step in the pipeline works, not just
*how*
.

You'll work alongside senior remote sensing scientists and software engineers to implement radiometric corrections, atmospheric compensation, spectral analysis algorithms, and geospatial data transformations. If you're the kind of person who can derive a reflectance equation on a whiteboard and then implement it as a clean, tested Python module—this role is for you.

Key Responsibilities

Pipeline Development

• Build, test, and maintain scalable data processing pipelines for satellite imagery—from raw sensor data through calibrated, orthorectified, analysis-ready products.
• Write modular, well-documented Python code that runs reliably in cloud environments (AWS), not one-off notebooks.
• Implement radiometric calibration, atmospheric correction, geometric orthorectification, and spectral resampling stages ...