Masterclass: Identifying & Engaging Top-tier Tech Talent

Sourcing top-tier tech talent is less about finding the objectively "best" candidate and more about finding the best fit for a specific role, budget, and company reputation at a specific moment. A JavaScript engineer who looks perfect on paper may be wrong for a client who can't afford top-of-market pay or who has a reputational problem driving away applicants in the first place.

A method called the Sourcing Sprint pairs two sourcers on the same requisition, borrowing the idea of pair programming from engineering teams. Working alongside a partner introduces healthy competition and accountability: sourcers start comparing notes, flexing what they know about a role, and checking their own ego before a shared status meeting. Kept away from toxicity, that dynamic pushes both people toward higher-quality lists than either would produce alone. The same pairing model works between a sourcer and a hiring manager, or between two recruiters splitting sourcing duties.

Layoffs at major tech companies create pressure to believe that top talent is suddenly easy to reach, but a candidate who takes a call because they're out of work doesn't automatically feel differently about a company or a role than they did before the layoff. Setting realistic expectations with hiring managers about who is actually reachable, and how they're likely to feel, prevents a sourcing project from being set up to fail.

On artificial intelligence: predictive analytics and AI research tools have a place, but only when used with precision and without oversell. The recommendation is to treat AI output the way sourcers once treated a Google search — filter it through professional judgment, then bring conclusions into a meeting as informed opinion rather than citing the tool itself.

Because no single platform reaches every candidate, using more than one — LinkedIn, GitHub, Stack Overflow, niche meetup communities, developer conversations on X — expands the pool and surfaces people who would otherwise stay hidden. Each platform carries a different level of visibility: a LinkedIn message is obviously recruiting, contact pulled from a scraped GitHub profile is less obvious, and showing up at a local developer meetup requires even more care. Genuine, specific outreach — referencing an actual repository or project rather than a generic pitch — gets stronger responses across all of them.