Hiring nowDrug discovery and molecular design

Protein Design Scientist

Also called AI Protein Designer

A Protein Design Scientist designs protein sequences and structures that meet defined functional targets, then plans the experiments to prove they work. They turn modelling into candidates a company can test, and into evidence it can trust.

What they do

Starts with a target property, such as binding, stability or expression, and works back to candidate sequences or structures. Runs structure predictors and design models, filters the outputs, and ranks candidates on function and developability. Writes the experimental validation plan with the lab team, including which assays, which controls and how many variants to order. Reads assay results, compares them with predictions and adjusts the next design round. Keeps notes on what failed and why. Checks biosafety and automation constraints before anything goes to the bench.

What a strong hire delivers

A first set of candidate proteins with a clear rationale for each, and a validation plan the lab can run without rework. A working loop where assay data feeds back into design choices. Early evidence on how far the models can be trusted for this target. Shared records that let colleagues repeat and challenge the work. Verified hits that meet function and developability criteria.

How you can tell they are good

Ask them to walk through a design project from target to tested result. Look for honest accounts of designs that failed in the lab and what they changed. Ask how they chose which candidates to order and how they would spot a model being confidently wrong. Strong people talk about controls, assay noise and developability, not only prediction scores.

Why it is hard to fill

The title varies between companies, so searches by title miss people. The skill sits between computational modelling and wet-lab reality, and few people have done both well. Many candidates have strong modelling papers but have never seen their designs tested. Others know the bench but not modern design methods. Candidates who have closed the loop from design to verified result are rare, and they are usually well placed already.

Where to find them

Look in computational structural biology, protein engineering, computational chemistry and ML for biology groups. Biotech and therapeutics start-ups, antibody and enzyme engineering teams, and academic design labs are common sources. Some come from pharmacology or from ML research and have moved towards molecular work. Frontier lab science teams and large pharma computational groups also produce good candidates. Check for authorship on experimentally validated designs.

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 Protein Design Scientist

  1. Describe a protein you designed that was tested in the lab. What was the target property and what happened?

    A strong answer names the property, the method, the hit rate and the surprises. They own failures and explain what changed next. Worry if results are vague, or if nothing was ever tested experimentally.

  2. How do you decide which designs go forward when you can only test a small number?

    Look for ranking on function and developability, diversity across candidates, and controls. Good answers include uncertainty and cost. Worry if the answer is simply highest model score.

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

Job description you can post

We are building AI-driven protein design into how we find new therapeutics. We need a scientist who can turn models into candidate proteins and prove they work in the lab. The work sits between computation and experiment, and you will shape both.

What you will do

  • Design protein sequences and structures against defined functional targets
  • Rank candidates on function and developability
  • Write experimental validation plans with the lab team
  • Analyse assay results and feed them into the next design round
  • Assess how reliable our models are for each target
  • Document methods and results so others can reproduce them
  • Apply biosafety checks before designs go to synthesis

What you will bring

  • Designs you have taken through to experimental testing
  • Hands-on use of molecular modelling and structure prediction
  • Sound experimental design, including controls and assay limits
  • Clear reasoning about developability trade-offs
  • Ability to work closely with wet-lab scientists
  • Honest accounts of what failed and why

Nice to have

  • Experience with lab automation and assay data systems
  • Background in antibody or enzyme engineering
  • Experience training or fine-tuning design models

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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