Hiring nowGenomics, multi-omics and biological data

Genomics Machine Learning Scientist

Also called Genomic AI Scientist

A Genomics Machine Learning Scientist builds models of genomic variation and sequence function, and shows they hold up across populations and assays. Without that, a company ships predictions that fail on the people and data it did not train on.

What they do

The work starts with a biological question about what a variant or sequence does. The scientist chooses or builds a sequence model, assembles training data from bioinformatics pipelines and omics databases, and trains it. Most of the effort goes into validation. They split data by ancestry, cohort and assay, look for leakage from related individuals or shared batches, and test whether a signal is real or an artefact. They also handle privacy constraints on genetic data. Findings go to biologists, engineers and product leads in plain terms, with clear limits on what the model can claim.

What a strong hire delivers

Within the first months, a strong hire delivers a working genomic model with a written validation plan that the team trusts. They set up reproducible data pipelines and held-out evaluation sets split by population and assay. They give leadership an honest account of where the model works and where it does not. They also leave a clear view of which next experiments are worth the cost.

How you can tell they are good

Ask them to walk through a model they built and how they tested it. A strong candidate explains how they split the data, what leakage they looked for, and what failed on a new population or assay. They talk about confounders such as ancestry and batch without prompting. They can state what their model should not be used for. Be wary of anyone who quotes only headline accuracy from a random split.

Why it is hard to fill

The title is new and the skills sit across two fields. Machine learning engineers often lack the genetics to spot confounding. Geneticists and bioinformaticians often lack modern sequence modelling experience. People who do both are few, and many are already settled in labs or well funded teams. Job titles vary a lot, so searching by title alone misses good candidates. We read the work they have actually done.

Where to find them

Look to computational biology and statistical genetics groups, bioinformatics teams in genomics and diagnostics companies, and academic labs working on sequence models. Titles include computational biologist, bioinformatics scientist, statistical geneticist and machine learning scientist in biotech. Some come from ML research and have moved into biology. Others come from genetics and taught themselves deep learning. Both routes can work.

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 Genomics Machine Learning Scientist

  1. Tell us about a genomic model you built. How did you decide it was valid beyond the data it was trained on?

    Covers the held-out design, such as splits by population, cohort or assay, and what the results showed. Good answers include a failure they found and fixed. Worry if validation was only a random split or they cannot describe any failure.

  2. How do you detect and prevent leakage when training on genomic data from related individuals or shared batches?

    Mentions relatedness, shared ancestry, batch and site effects, and splitting before any feature selection. They describe concrete checks. Worry if they treat leakage as a minor detail or only mention random shuffling.

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

Job description you can post

We are building models that predict the effect of genomic variation and sequence function. We need a scientist who can build those models and prove they hold up across populations and assays. You will work closely with biologists and engineers on questions that shape our product.

What you will do

  • Build and train models of genomic variation or sequence function
  • Design validation that holds out populations, cohorts and assays
  • Build and maintain reproducible bioinformatics data pipelines
  • Check for leakage, confounding and batch effects
  • Report model strengths and limits clearly to biologists and product teams
  • Work within privacy requirements for genetic data

What you will bring

  • Shipped or published a genomic or sequence model with documented validation
  • Strong grounding in probability and statistics
  • Experience building data pipelines over omics data
  • Working knowledge of genetics and population structure
  • Fluent with sequence modelling methods
  • Clear written and spoken communication with non-ML colleagues

Nice to have

  • Experience with biomolecular modelling
  • Privacy engineering for sensitive biological data
  • Work across multiple assay types

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
  • Data pipelinesAdvancedBuild reliable ingestion and transformation systems
  • Probability and statisticsWorkingQuantify variability and statistical evidence
  • Privacy engineeringWorkingLimit disclosure and unauthorised personal-data use

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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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