Hiring nowScientific machine learning and research computing

AI Research Software Engineer

Also called Research Software Engineer, AI

An AI Research Software Engineer turns scientific machine learning ideas into software that other people can run, trust and extend. They give a research-led company code that survives beyond one paper or one researcher.

What they do

They take research code from notebooks and one-off scripts and turn it into tested, documented packages. They build pipelines for training and evaluating learning models on scientific data, and they connect those models to differentiable simulators and numerical solvers. They run jobs on shared HPC clusters and keep results repeatable, down to seeds, environments and data versions. They review code from researchers, agree interfaces with scientists, and fix the awkward bugs where a model looks fine but the physics is wrong. They also sit with users and watch how they work.

What a strong hire delivers

In the first months, a strong hire gets one important research workflow running from a clean checkout, with tests and clear documentation. They put in place shared conventions for environments, data versions and experiment tracking. They cut the time scientists spend fighting tooling. They also leave behind a small set of checks that catch numerical and correctness errors before results are shared.

How you can tell they are good

Ask them to walk through a research codebase they made more reliable. Look for specifics: what broke, how they tested it, what they refused to compromise on. Ask how they would know a model's output is scientifically wrong when the metrics look good. Strong people talk about users, uncertainty and trade-offs. Be wary of anyone who only talks about model accuracy or framework choices.

Why it is hard to fill

The title is used loosely, so CVs mislead. Some candidates are strong engineers with no scientific grounding. Others are excellent scientists whose code only they can run. The people you want sit between the two, and many are happy in academic or lab settings with no reason to move. Job adverts that read like generic ML engineering roles put them off. Be clear about the science.

Where to find them

Look in university research computing groups, national laboratories and scientific software teams. Many come from physics, chemistry, biology, climate or engineering, then moved towards software. Others start in applied mathematics or scientific computing. Titles include Research Software Engineer, Scientific Software Developer, Computational Scientist and Research Engineer. Companies include AI-for-science startups, drug discovery groups, materials firms and frontier lab science teams.

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 Research Software Engineer

  1. Tell us about research code you made reproducible. What was wrong with it before and what did you change?

    A strong answer names concrete problems such as hidden state, unpinned environments or manual steps, and the fixes: tests, containers, data versioning. Worry if they describe only documentation or say reproducibility was someone else's job.

  2. How do you test code whose correct output is not known in advance?

    Good answers cover conservation checks, known limits, analytic cases, convergence tests, regression baselines and tolerance choices. Worry if they rely only on unit tests of shapes and types, or on comparing to a previous run.

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

Job description you can post

We build AI methods for scientific research, and we need an engineer to make our research software dependable. You will work closely with scientists and machine learning researchers to turn promising methods into tested tools that people can run, trust and build on.

What you will do

  • Turn research prototypes into maintained, tested packages
  • Build reproducible training and evaluation workflows for scientific models
  • Integrate learning models with simulators and numerical solvers
  • Run and debug large jobs on HPC systems
  • Set standards for environments, data versions and experiment tracking
  • Review code and coach researchers on good engineering practice
  • Work with scientists to make tools easy to use

What you will bring

  • Shipped scientific or research software that others use
  • Strong scientific Python and testing habits
  • Experience with deep learning in a scientific or numerical setting
  • Ability to check results for scientific correctness
  • Experience with HPC or large shared compute
  • Clear written communication with non-engineers

Nice to have

  • Experience with differentiable simulators
  • Background in uncertainty quantification
  • Contributions to open scientific software
  • Training in a scientific discipline

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
  • Uncertainty quantificationWorkingEstimate confidence, calibration and model uncertainty

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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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