Hiring nowScientific machine learning and research computing

Scientific Machine Learning Scientist

Also called SciML Scientist · AI Scientist (Domain) · AI-for-Science Researcher

A Scientific Machine Learning Scientist builds learned models that obey the physics, chemistry or biology of a problem. They matter because a model that fails outside its training data wastes experiments and erodes trust.

What they do

They take a scientific question and turn it into a model that can be trusted. That means choosing how to encode known constraints, such as conservation laws or symmetries, and deciding where learning should replace or correct a simulator. They train deep models, often alongside differentiable simulators, on HPC clusters. They design experiments and held-out tests that push the model beyond familiar conditions. They quantify uncertainty and write it up in notebooks and reports that domain scientists can read, question and reproduce.

What a strong hire delivers

Within the first months, a strong hire delivers a working scientific model with validation against both physical principles and real measurements. They set out where it is accurate and where it is not. They give the team calibrated uncertainty estimates, a reproducible training and evaluation set-up, and a clear view of which experiments or simulations the model can now save.

How you can tell they are good

Ask them to walk through one model they built and how they knew it was valid. Strong people talk about failure first: where it broke, how they found out, what they changed. They can explain why they chose a constraint, show uncertainty that was checked rather than assumed, and describe testing outside training conditions. Be wary of accuracy numbers with no account of validity.

Why it is hard to fill

Very few people are deep in both a scientific discipline and modern machine learning. Titles are inconsistent, so the same person may call themselves a SciML scientist, a domain AI scientist or an AI-for-science researcher. Many are academics or sit inside labs, and the best are rarely looking. Candidates strong in one half are common. Candidates strong in both are rare, and we screen carefully for that.

Where to find them

They usually come from a doctorate in a scientific discipline, applied mathematics or scientific computing, followed by hands-on machine learning work. Look in research labs, biotech, materials and chemistry startups, and computational groups within universities and industry. Common titles include computational scientist, research scientist, applied mathematician and numerical modeller who has moved towards learned methods.

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 Scientific Machine Learning Scientist

  1. Tell us about a model where you built a scientific constraint into the learning. Why that constraint, and what changed?

    A strong answer names a specific constraint, explains the trade-off between hard and soft enforcement, and shows measured impact. Worry if constraints are described only in general terms or if they added nothing testable.

  2. How do you test whether a model is valid outside its training conditions?

    Look for held-out regimes, extrapolation tests, comparison with physical limits and fresh experimental data. Good candidates expect failure and plan for it. Worry about reliance on random train-test splits alone.

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

Job description you can post

We are building learned models for scientific problems where being wrong is expensive. We need a scientist who can combine deep domain understanding with modern machine learning to produce models our researchers can trust, test and use to guide experiments.

What you will do

  • Develop learned models that respect physical and disciplinary constraints
  • Design validation against physical principles and empirical measurements
  • Quantify and report uncertainty in model predictions
  • Test model behaviour outside training conditions
  • Work with domain scientists and experimentalists to plan experiments
  • Run training and simulation workloads on HPC systems
  • Document methods and results in reproducible notebooks and reports

What you will bring

  • Demonstrated models built for a scientific problem, with validation you can show
  • Strong grounding in a scientific discipline, applied mathematics or scientific computing
  • Hands-on deep learning experience in scientific settings
  • Practical use of uncertainty quantification methods
  • Fluency in scientific Python and reproducible workflows
  • Clear written communication with non-ML scientists

Nice to have

  • Experience with differentiable simulators
  • Experience running large jobs on HPC clusters
  • Published or shared work on constraint-aware learning

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

Other roles in scientific machine learning and research computing

Hiring nowAI Research Software EngineerAn AI Research Software Engineer turns scientific machine learning ideas into software that other people can run, trust and extend. They give a…EmergingAI Scientific Workflow EngineerAn AI Scientific Workflow Engineer connects data, models and instruments into reproducible science pipelines. They make sure results can be traced…EmergingAI Reproducibility SpecialistAn AI Reproducibility Specialist independently reruns AI-supported scientific claims and reports what holds and what does not. They protect the…

Related roles in other families

Hiring nowComputer Vision EngineerComputer vision and spatial perceptionA Computer Vision Engineer builds detection and recognition systems that work in a defined setting, and proves how well they work. Companies rely on…Hiring now3D Vision ResearcherComputer vision and spatial perceptionA 3D Vision Researcher recovers spatial structure from images and depth data, then proves it holds up. Companies building robotics, mapping, AR or…Hiring nowVideo Understanding ScientistComputer vision and spatial perceptionA Video Understanding Scientist builds models that recognise actions, events and the dependencies between them in video. Companies hire for this…EmergingOpen-World Perception ResearcherComputer vision and spatial perceptionAn Open-World Perception Researcher builds vision models that know when they are looking at something unfamiliar. That matters because a system that…

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.

← Scientific machine learning and research computingAll new job families

Start a search

Hiring a Scientific Machine Learning Scientist?

Tell us the work. We find the people already doing it, whatever their job title says, and you meet them already interviewed for your role.