Hiring nowClimate, weather and Earth intelligence

Geospatial AI Engineer

Also called GeoAI Engineer

A Geospatial AI Engineer builds models and data products from satellite and other spatial observations. They help a company turn raw Earth data into forecasts and decisions that hold up in places the model has not seen.

What they do

Most days go on building and checking geospatial pipelines. That means ingesting Earth observation and weather model data, cleaning it, aligning it in space and time, and training models on it. They work on time series as well as images. They test whether results hold in new regions, not just new samples from the same ones. They check geolocation quality, chase down coverage gaps, and put uncertainty on outputs. They also explain to product and domain colleagues what the model can and cannot say about a given place.

What a strong hire delivers

In the first months, a strong hire delivers a working pipeline from raw observations to a usable data product, with documented sources and known gaps. They set up an evaluation that separates training and test areas so spatial leakage is visible. They give the team a clear read on where the model generalises, where it does not, and what coverage would fix it.

How you can tell they are good

Ask them to walk through a past project and explain how they split data by location, and why. Good candidates talk about spatial autocorrelation, sensor quirks, cloud cover, geolocation error and uncertainty without prompting. Ask what they would distrust in a headline accuracy number. Worry if every result comes from random splits or if they cannot say where a model fails.

Why it is hard to fill

The title is new and rarely matches what people have been called. The skill sits between remote sensing, climate science and machine learning, and few people are strong in all three. Many ML engineers have never dealt with spatial leakage. Many geospatial analysts have not shipped production pipelines. Candidates often look weaker on paper than they are because their best work was labelled research or analysis.

Where to find them

Look at people with titles such as remote sensing scientist, geospatial data scientist, climate data scientist, meteorology researcher and scientific ML engineer. They come from climate and weather groups, Earth observation companies, environmental analytics firms, research institutes and agricultural or energy teams. Some move over from general ML roles after working on satellite or mapping data.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Geospatial AI Engineer

  1. Tell us about a model you built from satellite or other spatial data. How did you decide it was good enough to use?

    Strong answers name the data, the evaluation and the decision it supported. They explain what was held out and why. Worry if the answer rests on one accuracy figure, or if the candidate cannot describe how the output was actually used.

  2. How do you split data for training and testing when nearby locations are similar?

    Look for blocked or region-based splits, awareness of spatial autocorrelation, and a view on how far apart is far enough. Worry if they default to random splits or treat leakage as a minor detail.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building models and data products from spatial observations to support environmental forecasts and decisions. We need a Geospatial AI Engineer who can take Earth observation and weather data from raw inputs to dependable outputs, and who is honest about where those outputs stop being reliable.

What you will do

  • Build and maintain pipelines that turn Earth observation and weather model data into model-ready datasets
  • Train and improve models for spatial and time-series prediction
  • Design evaluation that is independent across space, not just across samples
  • Monitor geolocation quality and data coverage, and fix gaps
  • Quantify and communicate uncertainty in model outputs
  • Work with domain scientists and product colleagues to shape data products
  • Document data sources, assumptions and known limits

What you will bring

  • Shipped work using remote sensing or other spatial data, which you can walk us through
  • Experience building spatially independent validation and spotting leakage
  • Solid time-series modelling skills
  • Working knowledge of Earth systems, climate or meteorology
  • Practical experience with geospatial tools and data formats
  • Experience applying uncertainty quantification to real outputs

Nice to have

  • Experience with numerical weather or climate model output
  • Experience running pipelines at scale
  • Published or shared work in Earth observation or scientific ML

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Geospatial and earth systemsAdvancedAnalyse spatial, temporal and environmental information
  • Time-series modellingAdvancedModel temporal dependencies and forecasts
  • Scientific domain expertiseWorkingApply disciplinary theory and experimental standards
  • Uncertainty quantificationWorkingEstimate confidence, calibration and model uncertainty

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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