About HumanSignal

Real-world data is the competitive edge in AI.

HumanSignal is a human data partner for companies building AI models and products. Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery.

We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the open-source standard for data labeling and evaluation, used by over 1 million practitioners worldwide.

We specialize in the operationally complex: real-world data collection, multimodal pipelines, and multi-step workflows. Advanced ML and AI teams use our enterprise platform to run their own data factories, and our services team to extend their reach where in-house capacity runs out.

If you want to do work that materially shapes how the next generation of AI products gets built, we'd love to talk.

Level: Individual Contributor
Compensation: $55,000 – $100,000
Location: San Francisco,



CA preferred; open to other remote options

About the Role

HumanSignal Services runs on expert talent — and we need a lot of it, fast. As our high-volume recruiter, you are the engine that keeps our data programs fully staffed with the right contributors at the right time. This isn't a post-and-pray recruiting role. You'll be proactive, scrappy, and relentless — sourcing, screening, and moving candidates through a pipeline that never stops. When a program needs 500 vetted domain experts in 72 hours, you're the one who makes it happen. Speed matters here, but so does quality — the wrong contributor wastes everyone's time and hurts the data. You'll own both.

What You'll Do

You'll run high-volume sourcing across multiple channels simultaneously — job boards, LinkedIn, niche communities, referral networks, and anywhere else qualified talent lives. You'll screen candidates quickly and accurately, matching domain expertise to program r

📌 High Volume (Ciudad de México)
🏢 HumanSignal
📍 Ciudad de México