Find the researcher whose work says more than their title.
Use publications, repositories, research artifacts, project evidence, and adjacent technical titles to find hard-to-classify AI talent.
“Applied AI researcher with public work on inference systems, model serving, or distributed training.”
AI and research sourcing that follows the work, not just the résumé title.
The strongest AI candidates may sit across research engineering, ML systems, infrastructure, applied science, or open-source communities. Conventional titles alone miss important evidence.
Separate hiring truth from search recall.
Discovery expansion should help you find more relevant people without silently becoming a new hiring requirement.
Capability is stated before the search runs.
Open research graph
Supported publication and research sources can return public scholarly evidence.
GitHub and public technical work
Repository and project evidence can expand beyond conventional profile search.
Licensed people data
Optional coverage layer when connected; it does not replace public evidence.
Know why a person surfaced.
Candidate relevance, verified facts, weak signals, contradictions, and missing evidence should remain distinguishable during review.
Artifact-aware discovery
Publications, repositories, and projects can create search paths conventional résumés do not expose.
Identity stays reviewable
Cross-source observations are not silently merged when identity remains uncertain.
Evidence survives ranking
Recruiters can inspect why each candidate surfaced and what is still missing.
Turn this market into a repeatable sourcing system.
Start with the role, keep source truth visible, inspect the evidence, and let approved recruiter feedback improve the next pass.