Deeptech (AI)

TalentHub recruiters can tell the difference between a candidate whose resume says "worked with LLMs" versus one who has actual research infrastructure experience — trained models from scratch or near-scratch, worked on pretraining, has publications where they're clearly a primary contributor, or built the eval/data pipeline infrastructure that applied ML actually depends on. That distinction is learnable and screenable, even without being an ML researcher ourselves.

We support the full stack, but with honest variation in depth: ML engineers, applied research roles, and commercialization/business hires are core strengths with solid existing pipelines, hardware engineering (RF, chip design, embedded, robotics) is a narrower specialist market where we can deliver but with less bench depth than software, and research scientist hiring is strong specifically where we have existing lab or postdoc relationships in your subfield.

We have done hiring for both tenured and non-tenured professorship positions (for example, TalTech’s different departments) as well as placements that involve pulling talent directly out of academia, and postdocs transitioning into applied research roles. So while we're not going to claim equal relationship strength in every research area or every country's academic system, we feel confident in approaching academic talent.