Pillar page · ML hiring
Machine learning hiring without vague job posts
Clarify outcomes, constraints, and evaluation before you source—so candidates self-select and your panel spends time on substance.
ML hiring fails at the brief
Most mismatches start with fuzzy scope: research vs shipping, offline vs online metrics, ownership of data and evals. Fix the brief and the rest of the funnel gets easier.
Guides for hiring managers
Editorial articles you can send to leadership and panelists before kickoff.
Blog
How to hire LLM engineers without guesswork
A practical playbook for defining LLM roles, writing job posts that self-filter candidates, structuring screens, and avoiding the buzzword trap—built for hiring managers and technical recruiters.
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Resource
Writing clearer AI job briefs
Practical patterns for job posts that attract relevant LLM, agents, and applied ML talent without buzzword soup.
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List roles candidates understand
Publish stack-clear jobs, then browse talent whose profiles show how they ship with models and automation.
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