Hiring nowDrug discovery and molecular design

AI Drug Discovery Scientist

Also called Drug Discovery ML Scientist

An AI Drug Discovery Scientist uses predictive models and experimental evidence to decide which targets and compounds are worth testing. They help the company spend scarce lab effort on the ideas most likely to hold up.

What they do

They build and run models that rank compounds or biological targets, then turn those rankings into hypotheses the lab can test. They work with chemists and biologists to choose assays, set controls and decide what a useful result looks like. They check structure predictions and molecular models against assay data, and note where the models fail. When results come back, they feed them into the next round of modelling. They keep data clean, track how each ranking was made, and explain the reasoning to scientists who will not read code.

What a strong hire delivers

Within the first months, a strong hire delivers a ranked set of experimental hypotheses that the lab agrees to test. They set up a clear way to compare predictions with assay results. They report validation outcomes honestly, including misses. They also leave behind a documented, repeatable workflow, so the team can rerun the ranking as new data arrives.

How you can tell they are good

Ask them to walk through a past project from model to bench result. A good candidate says what was predicted, what was tested, what came back and what they changed. Ask how they chose assays and controls. Listen for prospective results, not only retrospective benchmarks. Be wary of anyone who cites model scores but cannot explain the experiment.

Why it is hard to fill

The work sits between two worlds. Strong machine learning people often have not seen how noisy assays behave. Strong bench scientists may not build models. Titles vary widely, so searching on the job title misses many good people. Those with real prospective hit-rate experience are few, and most are already embedded in teams that value them. We usually search by evidence of work, not by title.

Where to find them

Look to computational chemists, structural biologists, pharmacologists and machine learning scientists who have worked close to a lab. They sit in biotech companies built around AI, in established pharmaceutical groups, in academic labs with strong translational links and in research institutes. Titles include computational chemist, molecular modeller, cheminformatics scientist and drug discovery 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 Drug Discovery Scientist

  1. Describe a project where your model's ranking was tested in the lab. What happened?

    A strong answer names the prediction, the assay, the hit rate and what they learned, including failures. Worry if they only cite retrospective benchmarks or cannot say what was actually tested.

  2. How do you decide which assay will give a trustworthy test of a model's prediction?

    They should discuss assay relevance, controls, noise, throughput and what a positive result means. Worry if they treat assays as a black box or ignore variability between runs.

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

Job description you can post

We use predictive models and experimental evidence to decide which targets and compounds deserve lab time. We are hiring an AI Drug Discovery Scientist to turn model outputs into ranked hypotheses, work with our bench scientists to test them, and use the results to improve each next round.

What you will do

  • Build and apply models to prioritise compounds and biological targets
  • Turn rankings into clear, testable experimental hypotheses
  • Design validation experiments with chemists and biologists
  • Analyse assay results and compare them with predictions
  • Report hit rates and reproducibility honestly, including misses
  • Maintain documented, repeatable modelling workflows
  • Explain methods and uncertainty to non-computational colleagues

What you will bring

  • Demonstrated work taking model predictions through to experimental validation
  • Hands-on experience with molecular modelling and structure prediction
  • Working knowledge of drug-discovery assays and their sources of noise
  • Strong experimental design and statistical judgement
  • Ability to write clean, reviewable code and track data versions
  • Clear communication with bench scientists

Nice to have

  • Experience with lab automation and assay data systems
  • Familiarity with biosafety practice
  • Background in computational chemistry, structural biology or pharmacology

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

Skills that matter

  • Biomolecular modellingAdvancedRepresent and predict molecular structure and interactions
  • Scientific domain expertiseAdvancedApply disciplinary theory and experimental standards
  • Experimental designWorkingChoose comparisons that identify credible effects
  • Lab automation and biosafetyWorkingIntegrate instruments and safe experiment execution

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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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