Hiring nowGenomics, multi-omics and biological data

AI Bioinformatics Engineer

Also called Bioinformatics ML Engineer

An AI Bioinformatics Engineer builds the pipelines that turn raw biological data into clean, trustworthy inputs for models. Without that work, a company's AI results cannot be reproduced or relied on.

What they do

Builds and maintains the workflows that move biological data from raw files to model-ready tables. That means ingesting sequence and omics data, writing checks for quality, format and sample mix-ups, and keeping every step versioned so results can be rerun. Works with data scientists and biologists to agree what a good input looks like. Connects sequence models to pipelines and public omics databases. Handles sensitive genetic data with care, including access controls and de-identification. Investigates odd results and traces them back to the data before anyone blames the model.

What a strong hire delivers

Within the first few months, a strong hire gives the team a pipeline that runs end to end and produces the same results every time. They add automated data checks that catch bad samples and format drift early. They document the workflow so others can run it. Scientists and ML engineers start trusting the data, and model debugging gets faster.

How you can tell they are good

Ask for a pipeline they built and have them walk through it. Good candidates explain how they version data and code, what checks they added and why, and a time the data itself was wrong. They talk about batch effects, reference versions and file formats without prompting. Be wary of people who only talk about model accuracy.

Why it is hard to fill

The title varies, and the skills sit across biology, software and ML. Most bioinformaticians are strong on analysis but less so on production engineering. Most ML engineers have never dealt with biological file formats or the quirks of omics data. Candidates who do both are rare, and they are often labelled by their last job rather than by what they can do.

Where to find them

Look at bioinformatics engineers and computational biologists in genomics companies, biotech, diagnostics and academic core facilities. Others come from ML engineering teams working on biological problems, or from genetics research groups with strong software habits. Data engineers who moved into life sciences are also worth a conversation, especially if they have handled regulated or sensitive data.

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 Bioinformatics Engineer

  1. Describe a bioinformatics pipeline you built. How did you make sure it was reproducible?

    Strong answers cover workflow tools, pinned software and reference versions, containers, tests and logged parameters. Look for specific examples of reruns matching. Worry if reproducibility means saving a script or a notebook.

  2. A model performs well in testing but badly on new samples. What do you check in the data first?

    Good candidates look at batch effects, sample provenance, label leakage, reference genome differences and preprocessing drift before touching the model. Worry if the first move is to retrain or tune.

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

Job description you can post

We are building AI that works with biological data, and we need someone to make that data dependable. You will build and run the pipelines that take raw sequence and omics data to model-ready inputs, with checks that let the whole team trust the results.

What you will do

  • Build and maintain reproducible bioinformatics pipelines for AI analysis
  • Write automated checks for data quality, format and sample identity
  • Version data, code and references so results can be rerun
  • Connect sequence models and omics databases to our workflows
  • Work with scientists and ML engineers to define what good input data looks like
  • Investigate unexpected results and trace them to their source
  • Protect sensitive genetic data through sound access and privacy practices

What you will bring

  • A pipeline you built and can explain end to end
  • Strong working knowledge of biological data formats and bioinformatics software
  • Solid probability and statistics, applied to real data
  • Experience building tested, versioned data pipelines
  • Awareness of batch effects and other sources of bias in omics data
  • Clear communication with both biologists and engineers

Nice to have

  • Experience with sequence or biomolecular models
  • Experience handling sensitive or regulated genetic data
  • Contributions to open bioinformatics software

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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