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Software, Machine Learning & AI Recruitment — TopHeads

AI-matched recruitment for machine learning, research and applied AI roles — research scientists, ML engineers, infrastructure and applied teams. Publication record and shipped-system record weighed together.

Hiring in machine learning and applied AI is difficult in a specific, structural way: the field's talent pool splits into people with a strong publication record, people with a strong shipped-systems record, and a small overlap — and most hiring processes are built to see only one of the three. TopHeads reads both records at once. A candidate's papers, citations and research lineage are assessed alongside what they have actually put into production, at what scale, and with what reliability obligations, so a research-heavy profile is not discarded by an engineering filter and a systems-heavy profile is not discarded by a research filter.

It also handles the vocabulary churn that makes this field so hostile to keyword search. Titles and framework names in AI turn over faster than in any other technical discipline, and a search built on the current season's terms systematically misses people whose work is directly relevant under a name that has since gone out of fashion. Because matching reasons over what the work was rather than what it was called, adjacent and predecessor experience is surfaced and explained rather than silently dropped.

For candidates, this is the part of the market where the interesting roles are least visible. Applied research groups, infrastructure teams inside non-technology companies, and early-stage teams that have not yet built a recruiting function rarely post anywhere a specialist would look. Describing the problem you want to work on — the modality, the constraint, the deployment reality — puts you in front of those mandates without a public job-search footprint.