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Founding Geospatial ML Engineer

Pluton Resource Intelligence | San Francisco, California, United States | 1mo ago
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Original posting (closed) below
full-time | on-site | lead
skills: python, postgis, postgresql, pymc, bayesian modeling, gdal, geopandas, aws, fastapi, spatial data pipelines, geospatial, coordinate systems, raster processing, vector processing, spatial joins

APPLY HERE - https://www.pluton.earth/careers/geospatial-ml-engineer

The Role:

NOUS is the brain of Pluton — the ML system that ingests heterogeneous geoscience data, scores prospectivity across geographic regions, and outputs decisions that drive physical hardware deployment in the field. We're building this across multiple regions and commodities globally. This role owns the system permanently — not as a contractor, not as a lead on a team of ten. You are the system's architect, builder, and long-term steward.

What NOUS Does:

  • Ingests raw geoscience data from multiple sources — satellite, field sensors, public databases — across formats that were never designed to work together
  • Scores geographic grid cells across multiple geological layers and combines them into a single prospectivity signal
  • Classifies each cell into a deployment decision — where to fly, where to sample, where to walk away
  • Updates in real time as new field data arrives, region by region, commodity by commodity

  • What We're Looking For:

    • Deep geospatial experience — PostGIS, coordinate systems, raster and vector processing, spatial joins at scale. This is not a nice-to-have.
    • Bayesian modeling — specifically the ability to build probabilistic models that learn from sparse, noisy, heterogeneous spatial data and produce calibrated outputs
    • Comfort with messy reality — field sensor data is incomplete, misprojected, and undocumented. You build pipelines that handle it without human intervention
    • Long-term ownership mentality — you're not here to ship a feature. You're here to own the most important system in the company and grow it over years, not months
    • Agency — you diagnose what's wrong, decide what to build, and execute without being managed step by step

  • The Stack:

    Python, PostGIS / PostgreSQL, PyMC / Bayesian modeling, GDAL, GeoPandas, AWS, FastAPI, Spatial data pipelines

    What This Is Not:

    • A research role — the output is deployed in the field, not published
    • A team with established process — you'll set the foundation others eventually build on
    • A stable data environment — regions, formats, and requirements change constantly

    How to Apply:

    https://www.pluton.earth/careers/geospatial-ml-engineer

    Click Apply Now and submit your application directly through our careers page. Along with your resume, please include:

    • A brief intro — who you are and why this role interests you
    • Links to work you're proud of — GitHub, live projects, or anything deployed in the real world
    • One example of a geospatial or probabilistic modeling project you've built and what it actually did

    Benefits

    health insurance
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