Hiring nowChemistry, materials and process discovery

AI Computational Chemist

Also called Computational Chemistry ML Scientist

An AI Computational Chemist builds and validates learned models that stand in for expensive chemical calculations. The work lets a company explore molecules, materials and reactions faster, with results it can trust against experiment.

What they do

The work sits between quantum chemistry and machine learning. They train learned potentials and property models on reference calculations, then test where those models hold and where they break. They set up simulation workflows, run molecular dynamics, and decide which new calculations to run to fill gaps in the data. They check outputs against trusted methods and lab measurements. They also work with experimentalists to choose which candidates are worth making, and they document the limits of each model so others use it sensibly.

What a strong hire delivers

In the first months a strong hire delivers a working model or simulation workflow that colleagues can run without them. It is validated against trusted calculations and, where possible, experiment. They also leave a clear record of where the model is reliable, a plan for improving the data, and a shortlist of candidates or conditions the team can act on.

How you can tell they are good

Ask them to walk through a model they built and how they knew it was right. A strong candidate talks about failure cases, out-of-domain behaviour and how they chose reference data. Ask what they did when the model disagreed with experiment. Worry if they quote only average error on a held-out set and cannot say what the model gets wrong.

Why it is hard to fill

Candidates rarely carry this title. Most are chemists who learned machine learning, or ML engineers who picked up chemistry, and each group tends to be weak on the other half. Real depth in both is scarce. Job titles vary widely, so searching by title misses many of the best people. We usually search by the work they have done instead.

Where to find them

Look to computational chemistry and materials modelling groups in universities and national labs, and to researchers moving from postdoctoral work into industry. Some come from drug discovery, battery, catalyst or specialty chemicals teams. Others are scientific ML researchers at AI-for-science start-ups or frontier labs. Typical titles include computational chemist, materials scientist, molecular modeller and ML 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 an AI Computational Chemist

  1. Tell us about a learned model you built for chemistry. What did it replace, and how did you validate it?

    A strong answer names the reference method, the data used and the checks against calculation and experiment. It should state clear limits. Worry if validation was only a random train and test split or if limits are vague.

  2. How do you decide which new reference calculations to run to improve a potential?

    Look for active learning ideas, uncertainty estimates, coverage of configurations and cost awareness. Good answers balance accuracy with compute budget. Worry if the answer is simply to gather more data.

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

Job description you can post

We are hiring an AI Computational Chemist to build learned models that approximate costly chemical calculations. You will work with chemists and ML engineers to turn those models into validated workflows that guide what we make and test next.

What you will do

  • Build and train learned potentials and property models on reference calculations
  • Design simulation workflows and run molecular dynamics at scale
  • Validate models against trusted calculations and experimental data
  • Choose new calculations to improve data coverage and reduce uncertainty
  • Work with experimental colleagues to prioritise candidates
  • Document model limits and share workflows so others can use them

What you will bring

  • Demonstrated work combining computational chemistry methods with machine learning
  • Experience training and evaluating deep learning models on scientific data
  • Strong grounding in chemistry, materials science or chemical engineering
  • Practical experience with molecular simulation and scientific computing
  • Ability to explain model failures and uncertainty clearly
  • Experience comparing computed results with experiment

Nice to have

  • Experience with active learning or optimisation for experimental design
  • Familiarity with materials databases
  • Work on reactive chemistry or catalysis

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

Skills that matter

  • Scientific domain expertiseAdvancedApply disciplinary theory and experimental standards
  • Deep learningAdvancedBuild and diagnose trainable neural models
  • Experimental designWorkingChoose comparisons that identify credible effects
  • 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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