Hiring nowClimate, weather and Earth intelligence

Climate AI Researcher

Also called Climate ML Scientist

A Climate AI Researcher studies climate processes and impacts using learned models. They give a company analysis it can defend, with honest uncertainty and checks against the physics, so forecasts and spatial decisions rest on solid ground.

What they do

They frame a climate question, choose the data and build a learned model to study it. That might mean time-series work on observations, or spatial models built on earth observation and weather model output. They compare results against known physical behaviour, test where the model breaks, and quantify how unsure it is. They work with engineers who run the pipelines and with domain experts who challenge the findings. They write up methods and limits so others can reproduce and question the work. Much of the job is deciding what a model can honestly claim.

What a strong hire delivers

In the first months, a strong hire delivers a climate analysis the team can trust, with clear uncertainty and physical checks built in. They set up a validation approach that does not depend on their own judgement. They document data choices and known failure cases. They also help the company decide which questions learned models can answer and which they cannot.

How you can tell they are good

Ask them to walk through a past analysis from question to result. Listen for how they validated it independently, what physical checks they used and where the model failed. A good candidate states uncertainty plainly and explains what they would not claim. Be wary of strong headline scores with no discussion of physical plausibility, or of confidence that ignores how the data was collected.

Why it is hard to fill

The title is used loosely, so candidates vary widely. Few people are strong in both climate science and modern machine learning. Climate scientists often lack ML depth, and ML engineers often lack physical intuition. Job titles rarely show which side someone leans towards. We screen on past work and how they handle uncertainty, not on the label. The best people are often settled in research posts and need a clear scientific reason to move.

Where to find them

They come from climate science, meteorology, atmospheric and ocean research, geospatial analysis and scientific machine learning. Look in university research groups, national weather and climate services, and earth observation teams. Some work at climate analytics firms, insurers and environmental data companies. Others sit in applied research teams at AI companies. Titles include Climate ML Scientist, research scientist and applied scientist.

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 Climate AI Researcher

  1. Describe a climate analysis you built with a learned model. How did you validate it independently?

    A strong answer names a held-out test that the candidate did not control, such as other regions, periods or observation sources. It explains what the validation showed and where it failed. Worry if validation was only a random split on the training data.

  2. How do you check that a model's output is physically plausible?

    Good answers cover conservation laws, known relationships between variables, sensible extremes and behaviour outside the training range. They give a concrete example of catching an implausible result. Worry if plausibility means only that error metrics looked fine.

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

Job description you can post

We are looking for a Climate AI Researcher to study climate processes and impacts using learned models. You will produce analysis that is clear about uncertainty and checked against the physics, so that our forecasts and spatial decisions can be trusted by customers and by our own team.

What you will do

  • Frame climate questions that learned models can answer
  • Build and test models on earth observation, weather model and other spatial data
  • Quantify uncertainty and report it plainly
  • Check outputs against physical behaviour and known processes
  • Design independent validation that others can rerun
  • Work with engineers and domain experts to turn findings into product decisions
  • Document methods, assumptions and failure cases

What you will bring

  • Demonstrated climate or earth systems analysis using machine learning
  • Experience with time-series modelling on real observational data
  • Practical use of uncertainty quantification methods
  • Working knowledge of geospatial data and tools
  • Clear examples of validating results independently
  • Able to explain limits of a model to non-specialists

Nice to have

  • Experience combining physics-based models with learned models
  • Published or shared research, code or analysis
  • Experience with extreme events or impact modelling

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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