Hired Up: Fixing your AI search results that miss the mark
AI hiring tools are supposed to save you time. But if you're still manually screening hundreds of applications, guessing why AI sourcing results are off, or discovering that a "great candidate" might not be a real person, the time savings aren't happening yet. In this episode, SJ Niderost (Content at Gem) sits down with Sam Sorkin (Scaled Customer Success Manager at Gem) to walk through how teams are actually solving these problems in one connected workflow.
Featuring
SJ Niderost
Content Marketing Manager, Gem
Sam Sorkin
Scaled CSM, Gem
What you'll learn:
- How to tell AI exactly who you're looking for, and what to change when results come back wrong (the difference between a 27-person pool and a 10,000-person pool is often one setting)
- Why the same criteria you build for sourcing should automatically score your inbound applicants, so you stop re-doing work at every stage
- How to use AI search data in hiring manager conversations that's grounded in who actually exists in the market, not who they wish existed
- How to catch fake candidates before you waste interview slots
Who should attend: Recruiters, sourcers, and TA leaders dealing with high application volume, niche sourcing roles, or growing concern about candidate fraud, who want to see what it looks like when sourcing, screening, and fraud detection work together instead of in silos.