domain
Recruiter
San Francisco, CA, US
Onsite
2026-06-30
Announced salary
$125,000 - $150,000
Low
$146K
Median
$188K
High
$248K
Market in San Francisco · BLS OEWS 2025
Estimated net pay
$7,451 - $8,681
/month · 28% withheld
after tax & contributions · Single, no dependents
Job description
**About the Role**
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We're hiring our first in\-house recruiter to help us scale a small, talent\-dense team without diluting the bar. The bar is high: Vals is small enough that one wrong hire is felt by everyone, and one great hire reshapes a team.
The ideal candidate likely has a nontraditional background for a recruiter — technical foundation, ideally CS or engineering, who moved into talent because they realized that finding and closing the right people is the highest\-leverage thing they can do at an early\-stage company. You'll partner directly with the founders and hiring managers to source, screen, and close engineers, researchers, and GTM hires.
You'll own pipelines end\-to\-end: writing JDs that pull the right candidates, sourcing the top of the funnel yourself, running first\-round calls, managing candidates through technical loops, and helping us close offers against competitive packages from frontier labs. You're joining as the first dedicated talent hire at a company. Motivated people will own more of this function over time — building out the team, the playbook, and the bar — with a pathway to grow into Head of People as we scale.
### **Requirements**
* 1\+ years of in\-house recruiting at a high\-growth startup, ideally in AI/ML, infra, or developer tools.
* A track record of closing engineers or researchers against competitive offers, including offers from frontier AI labs.
* Conversational familiarity with what's going on in AI. You should be able to sustain a conversation with technical talent and speak credibly about the labs, the work and where the field is heading.
* Strong written communication.
* Ability to work in\-person, in San Francisco.
### **Nice to haves**
* A technical background (CS or engineering degree, prior SWE experience).
* Prior experience hiring ML researchers or engineers with publications.
* Experience setting up scrappy recruiting ops (sourcing tools, ATS selection, interview rubrics) from zero.