Hiring nowNeuroscience, cognitive science and brain interfaces

Cognitive AI Researcher

Also called Computational Cognitive Scientist

A Cognitive AI Researcher tests whether computational accounts of thinking actually hold up against behaviour. They give the company models that explain what a system does and evidence that the explanation travels beyond one task.

What they do

Builds computational models of a cognitive capability, such as attention, memory, learning or language use, and works out what each model predicts. Designs controlled behavioural experiments to test those predictions against people, and against the systems the company builds. Cleans and analyses behavioural and neural recordings with signal processing and neural data toolchains. Compares competing accounts, drops the ones that fail, and writes up what is left in terms engineers and product leads can use. Works closely with machine learning staff on what a result means for the system.

What a strong hire delivers

In the first months, a strong hire delivers a working cognitive model of one capability the company cares about, plus a controlled behavioural comparison that shows where the model and real behaviour agree and where they part. They also leave a clear experimental protocol the team can rerun, and a short written view on which explanations are worth pursuing next.

How you can tell they are good

Ask them to walk through one model they built and how they tested it. A strong candidate states what would have proved the model wrong, explains how they controlled for confounds, and shows the result held on a second task. Ask what they dropped and why. Worry if they only describe fit to one dataset or lean on impressive terminology.

Why it is hard to fill

The work sits between two worlds. Cognitive scientists often lack production machine learning habits, and machine learning researchers often lack experimental discipline. Titles vary widely, so searching by job title misses most of the good people. Few candidates are strong at both modelling and experimental psychology, and many who are remain in academia. We usually search by method and publication record rather than title.

Where to find them

They come from cognitive science, computational neuroscience, experimental psychology, biomedical engineering and machine learning research groups. Many hold the title Computational Cognitive Scientist or research scientist in a university lab or institute. Others sit in neurotechnology firms, speech and audio teams, and the research arms of AI companies. Look for people with experimental papers alongside code.

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 Cognitive AI Researcher

  1. Describe a cognitive model you built. What did it predict that a simpler account would not?

    A strong answer names a specific, testable prediction and the rival account it beat. Listen for clear reasoning about why the extra complexity was justified. Be wary of models described only by how well they fit existing data.

  2. How did you design the behavioural experiment to compare your model with human performance?

    Look for control conditions, sample reasoning, counterbalancing and a plan for confounds. Good candidates explain what result would have falsified the model. Worry if the design was borrowed without understanding why it works.

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

Job description you can post

We are looking for a Cognitive AI Researcher to test computational accounts of cognition against real behaviour. You will build models, design controlled experiments and tell us which explanations hold up. Your findings will shape how we understand and improve our systems.

What you will do

  • Build computational models of cognitive capabilities and derive testable predictions
  • Design and run controlled behavioural experiments
  • Analyse behavioural and neural data using signal processing and standard toolchains
  • Compare competing accounts and report where each succeeds or fails
  • Check that findings generalise across tasks
  • Work with machine learning colleagues to turn results into system decisions

What you will bring

  • Published or shared work showing a cognitive model tested against behavioural data
  • Demonstrated skill in experimental design and statistics
  • Experience analysing behavioural or neural recordings
  • Working code for modelling and analysis
  • Clear written explanations of results for non-specialists
  • Background in neuroscience, cognitive science, biomedical engineering or machine learning

Nice to have

  • Experience with speech and audio modelling
  • Familiarity with privacy-aware handling of participant data
  • Deep expertise in a scientific domain relevant to our work

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
  • Experimental designAdvancedChoose comparisons that identify credible effects
  • Speech and audio modellingWorkingRecognise, synthesise or analyse audio
  • 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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