First In-House Recruiter - High-Growth AI Startup

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.

• 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.

• A technical background (CS or engineering degree, prior SWE experience).

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• Prior experience hiring ML researchers or engineers with publications.
• Experience setting up scrappy recruiting ops (sourcing tools, ATS selection, interview rubrics) from zero.

• Highly competitive salary and meaningful equity. Excellence is well rewarded.
• Relocation and transportation support
• Health/dental insurance coverage
• Lunch and dinner provided, free snacks/coffee/drinks
• 401K plan
• Unlimited PTO

Founding team: The core methodology behind this platform comes from NLP evaluation research we had done at Stanford. We raised a $5M seed from some of the top institutional and angel investors in the valley. Our team has prior work experience at NVIDIA, Meta, Microsoft, Palantir and HRT. Collectively, we have over 300 citations in our published work. Our early team include Stanford PhDs, ex-Jane Street quants, and the first designer at Snorkel.

Tech stack: We use Python for most things at Vals. Our platform is built on Django, with a React frontend. All of the infra is on AWS using CDK for IaC.

• Learning velocity: The role encompasses a wide variety of tasks. Rather than expecting you to be an expert on Day 1, we are looking for someone who can learn new skills and technologies extremely quickly.
• Ownership: Working in a small, talent-dense team, we expect everyone to show initiative to build where it's needed, not where it's asked. We strive for autonomy over consensus. This is especially true for this role.
• Intensity: The LLM landscape is constantly changing. Foundation model labs are continuously pushing the frontier. The unicorn companies that will emerge from this technology shift are being built now. Those that win will have an incredibly high speed of execution.
• Solution-oriented mindset: We're looking for people who see opportunities to craft solutions at each juncture, not those who pass hard problems to others or admit defeat.

• Hugging Face blog on evaluation
• Anthropic’s blog on challenges in evaluation
• New York Times article on issues in benchmarking
• Stanford HAI report showing hallucinations in legal tech tools

Know someone who would be a good fit? Connect them with rayan@vals.ai. If we hire them and they stay on for 90 days you’ll get a $10,000 referral bonus and Vals AI merch! Please mention the bonus in your email.