ScenarioAutonomous scientific discovery governance

Machine Discovery Verification Specialist

Also called Autonomous Discovery Auditor

A Machine Discovery Verification Specialist would independently check claims made by broadly autonomous research systems. The role depends on how AI develops, and would matter because no one should act on an unverified discovery.

What they do

They would take a discovery claim from an autonomous research system and try to reproduce it without relying on the system's own account. They would read the system's methods, design an independent experimental plan and agree it with the lab. They would check that experiments run safely and within authorisation. They would run or oversee the replication, compare results with the claim, and look for gaps, shortcuts and untested assumptions. They would then write up what held, what failed and what remains unknown, in terms a leadership team can act on.

What a strong hire delivers

In the first months, a strong hire would set up a verification process that sits apart from the team producing discoveries. They would agree what counts as a confirmed finding, run first replications end to end, and produce clear claim assessments with firm boundaries. They would also leave behind a record of how each verdict was reached, so decisions can be defended later.

How you can tell they are good

Ask for a time they reproduced someone else's result and it did not hold. Listen for how they found the cause and how they reported it. Ask them to design a replication for a claim they cannot fully inspect. Strong people separate evidence from assertion, name what they could not test, and are comfortable saying a claim is only partly confirmed.

Why it is hard to fill

If this role appeared, almost nobody would have done it, so titles would mean little. It needs senior scientific judgement, assurance discipline, and comfort with automated labs and biosafety. Those skills usually sit in different people. Scientists rarely have audit experience, and auditors rarely have the domain depth. Independence also matters, which narrows who companies can trust.

Where to find them

Today they would come from scientific leadership, research assurance, quality and compliance in regulated laboratories, and laboratory safety or biosafety. Some would come from reproducibility and research integrity work, or from AI governance and assurance teams. Look in academic labs, pharmaceutical and biotech research, contract research organisations, and groups evaluating frontier model capabilities.

Interview questions for a Machine Discovery Verification Specialist

  1. Tell me about a result you tried to replicate that did not hold. What did you do next?

    Strong answers describe a methodical search for causes, fair reporting to the original team, and a clear conclusion. Worry if they blame others quickly, never reproduce anything themselves, or treat a failed replication as a personal contest.

  2. How would you decide what counts as a confirmed finding from an autonomous research system?

    Look for explicit criteria, independent data, predefined success tests and stated limits. A good answer separates confirmed, plausible and untested. Worry if they accept the system's own reasoning as evidence or set no clear threshold.

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

Job description you can post

We are building capability to check scientific discoveries produced by autonomous research systems. We need someone who can independently replicate claims, judge the evidence and tell us plainly what is confirmed and what is not. You would work apart from the teams generating the results.

What you will do

  • Design and run independent replications of claims from autonomous research systems
  • Assess each claim and state clearly which findings are confirmed
  • Review experimental methods, data and assumptions for gaps and shortcuts
  • Check that experiments run within safety limits and authorisation
  • Work with domain experts when claims fall outside your field
  • Document verification decisions so they can be audited and defended
  • Advise leadership on how far a result can be relied on

What you will bring

  • Demonstrated independent replication or verification of complex scientific work
  • Strong scientific judgement in at least one research domain
  • Experience with research assurance, quality or integrity processes
  • Working knowledge of laboratory safety, and ideally biosafety
  • Clear written reporting of uncertain results to senior readers
  • Ability to hold an independent position under pressure

Nice to have

  • Experience with laboratory automation
  • Background in AI governance or model evaluation
  • Experience leading a research group

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Depends on a scenario

End-to-end automated scientific discovery

What AI would need to do
Systems autonomously formulate, execute and verify novel scientific work across disciplines.
What organisations would need to decide
Labs can safely connect models to instruments with independent validation and authorisation.
What would rule it out
Scientific novelty, reproducibility or safe experiment execution remains unreliable.

We give no date. The role becomes real if both conditions hold.

Skills that matter

  • Scientific domain expertiseAdvancedApply disciplinary theory and experimental standards
  • Lab automation and biosafetyAdvancedIntegrate instruments and safe experiment execution
  • AI governance and assuranceWorkingAssign accountability and verify lifecycle controls
  • Frontier scenario analysisWorkingExplore conditional outcomes without claiming predictions

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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. Scenario roles describe responsibilities that depend on stated conditions; they are not forecasts.

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