Jobs Companies HumanSignal Talent Sourcer, Expert AI Trainers

Über diese Talent Sourcer, Expert AI Trainers Stelle bei HumanSignal

HumanSignal · 🌍 Weltweit remote · Remote

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.

About the Role

As a Growth Associate, you'll build the expert supply side of our Data Services business. You'll source, vet, and onboard specialized annotators and performers across dozens of skill profiles and languages. Your pipeline decides how fast we can say yes to a new customer project, which makes your work a direct input to revenue.

What You'll Do

  • High touch sourcing and recruiting. Use LinkedIn Recruiter, professional communities, universities, and niche networks to find specialists: languages other than English, regional dialect speakers, designers, clinicians, and other hard-to-find profiles.
  • Scale sourcing channels. Leverage AI workflows and automation to test and scale channels that convert, from online communities to LinkedIn, while raising the quality bar and annotator experience. 
  • Establish vetting pipelines. Determine how to vet and assess annotators based on project requirements, expertise, and needs. 
  • Build relationships. Keep experts engaged between projects so our roster stays warm and staffable, not a list of cold emails.
  • Collaborate across teams. Partner with project and delivery leads to translate customer specs into candidate profiles early, so sourcing starts before the project does.
  • Drive growth. Staffed capacity is the constraint on how much work we can take. You loosen that constraint.

What We're Looking For

  • 2-5 years of experience in high-volume recruiting
  • Experience using LinkedIn Recruiter / Sales Navigator and other sourcing tools
  • Fluency building AI workflows to scale what’s working and improve anntotator experience 
  • Comfort owning funnel metrics: outreach volume, response rates, conversion, time-to-fill
  • Strong written communication, responsiveness and professionalism. Most of your outreach happens over email and DMs
  • A track record in fast-paced, target-driven environments (startups, agencies, consulting, or similar)
  • Resilience, proactivity, and flexibility to adjust strategy when things aren’t working or projects change course
Bereit, sich bei HumanSignal zu bewerben?
Bei HumanSignal bewerben

Über HumanSignal

We build software that helps people do what only they can do – give meaning.

Data fills our modern world. It flows prolifically inside organizations, customer interactions, product usage, environmental research, healthcare imaging, and beyond. What if we could use any of this historical data to predict the future? In most cases, we can now make predictions through Machine Learning & AI, but to do so in a meaningful and impactful way, historical data needs to be accurate, comprehensive, and without bias.

 

To make the best predictions, we believe teams with domain expertise should be responsible for annotating and curating data. It's called data labeling, and it’s a process of real people giving meaning to the information they see on the screen. HumanSignal was founded to take data labeling operations to the next level and help data scientists make better predictions.

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