Hiring nowGeoscience, mining and water systems

Mining AI Engineer

Also called Mining ML Engineer

A Mining AI Engineer brings AI assistance into approved mining workflows and tests it in the field. It matters because a model only earns its place if operations can use it safely and within environmental limits.

What they do

Takes mining problems that suit learned models and builds analytics or automation support for them. That might be flagging likely ground or water issues from geological and geophysical data, or helping planners choose between options under constraints. Works with GIS layers, geophysical outputs and hydrological models in scientific Python. Agrees with site teams which workflows are approved for AI assistance and which are not. Runs field tests, records where the model is wrong, and adjusts the tool and its guardrails. Writes down uncertainty in terms operators can act on.

What a strong hire delivers

Within the first months, a strong hire delivers one working piece of analytics or automation support, tested on site with real operators. They agree clear limits on where it can and cannot be used. They show how uncertainty is reported and how safety and environmental constraints are respected. They leave site teams able to say whether the tool helps their day.

How you can tell they are good

Ask them to walk through a model they put in front of operational users. Listen for what failed in the field, not just what worked. A good candidate explains how they handled uncertainty, who signed off the workflow, and what the tool was not allowed to do. Ask them to read a map or a hydrological output and say what they would distrust.

Why it is hard to fill

The title is rare and the people come from mixed backgrounds. Most candidates are either strong geoscientists or mining engineers who code a little, or ML engineers who have never stood on a site. Few have done both and shipped. Job titles vary widely, so searching by title misses many good people. Risk-aware judgement is hard to see on a CV.

Where to find them

Look at geologists, hydrologists, geophysicists and mining engineers who moved into data work, and ML engineers who spent time in resources or earth science. They sit in mining operators, mining technology and services firms, geoscience consultancies, exploration teams, water and environmental consultancies, and research groups. Titles often read as geodata scientist, mining data scientist or operations analytics engineer.

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 Mining AI Engineer

  1. Tell us about a model or tool you put in front of operational users in a high-risk setting. What happened?

    Strong answers describe real users, a field test and specific failures. They cover what changed afterwards. Worry if the story stops at offline accuracy or if no operator ever used the tool.

  2. How do you decide whether a mining workflow is suitable for AI assistance?

    Look for consideration of safety impact, cost of errors, data quality and human sign-off. Good candidates name workflows to leave alone. Worry about enthusiasm to automate everything.

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

Job description you can post

We are looking for a Mining AI Engineer to bring AI assistance into approved mining workflows. You will build analytics and automation support, test it in the field with operators, and make sure it works within our safety and environmental constraints.

What you will do

  • Build analytics or automation support for approved mining workflows
  • Work with geoscience and operations teams to choose suitable use cases
  • Run field tests and record where the tool succeeds and fails
  • Combine GIS, geophysical and hydrological data in scientific Python
  • Report uncertainty in forms operators can act on
  • Document limits, approvals and guardrails for each tool

What you will bring

  • Shipped a model or tool used by operational teams, with evidence of field testing
  • Strong scientific Python and geospatial skills
  • Working knowledge of geology, hydrology, geophysics or mining engineering
  • Experience with uncertainty quantification in practical decisions
  • Familiarity with optimisation or operations research methods
  • Sound judgement on safety and environmental risk

Nice to have

  • Experience on or close to an operating mine site
  • Experience with hydrological modelling
  • Experience working with agent frameworks or model providers

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
  • Scientific domain expertiseAdvancedApply disciplinary theory and experimental standards
  • Uncertainty quantificationWorkingEstimate confidence, calibration and model uncertainty
  • Optimisation and operations researchWorkingChoose actions subject to objectives and constraints

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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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