
HumanSignal enables data science teams to build AI models with their company DNA. With the emergence of generative AI, it’s more important than ever to build highly differentiated models by guiding…

HumanSignal enables data science teams to build AI models with their company DNA. With the emergence of generative AI, it’s more important than ever to build highly differentiated models by guiding…
Product: Label Studio — open-source and enterprise data labeling/annotation platform
Founded: 2019
Rebrand: Rebranded from Heartex to HumanSignal in June 2023
Users / community: Label Studio community: over 100,000–250,000 users (reported figures vary)
Latest known funding: Series A led by Redpoint (May 18, 2022)
Data labeling, dataset creation, model training/validation, human-in-the-loop AI
2019
Software Development
Seed round reported in early January 2021
25,000,000
Series A led by Redpoint with participation from Unusual Ventures, Bow Capital, Swift Ventures and angels
“Redpoint led Series A; other investors include Unusual Ventures, Bow Capital, Swift Ventures”
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The future of AI — whether in training or evaluation, classical ML or agentic workflows — starts with high-quality data. At HumanSignal, we're building the platform that powers the creation, curation, and evaluation of that data. From fine-tuning foundation models to validating agent behaviors in production, our tools are used by leading AI teams to ensure models are grounded in real-world signal, not noise.
Our open-source product, Label Studio , has become the de facto standard for labeling and evaluating data across modalities — from text and images to time series and agents-in-environments. With over 250,000 users and hundreds of millions of labeled samples, it's the most widely adopted OSS solution for teams working on building AI systems.
Label Studio Enterprise builds on that traction with the security, collaboration, and scalability features needed to support mission-critical AI pipelines — powering everything from model training datasets to eval test sets to continuous feedback loops.We started before foundation models were mainstream, and we're doubling down now that AI is eating the world. If you're excited to help leading AI teams build smarter, more accurate systems — we'd love to talk.
About the Role We're looking for an ambitious AI Engineer to transform how our go-to-market team operates . San Francisco, Austin, or Lisbon are preferred locations where you can collaborate with team members, but the position is remote or hybrid.
As the founding GTM engineer, you will architect and build the AI-powered systems that power our entire GTM motion, owning the technology stack, partnering with stakeholders to define the workflows, deploying AI agents and apps, and continuously improving how the company acquires, activates, and grows customers.
This is a highly hands-on role for someone who enjoys shipping systems, experimenting rapidly, and solving messy real-world problems with software. Why This Role Matters This role is a force multiplier for the entire company. You'll build the infrastructure that allows the GTM team to move faster, learn faster, and scale without proportional headcount. You'll help us:
What You'll Do Build & Automate the foundational GTM AI stack
AI-Powered Systems & Agents Design and deploy AI-driven workflows and agents that augment GTM teams.
Examples include:
You'll continuously improve these systems using real performance data and feedback loops. Growth: PLG + Sales-Assisted Motion
Experimentation & Learning Build the infrastructure that allows the GTM team to learn quickly and iterate with confidence.
Feedback Loops & Continuous Improvement Create tight feedback loops between users, product signals, and GTM actions.
What We're Looking For Core Skills This is fundamentally an engineering role , not traditional GTM operations. We're looking for someone who enjoys building systems, not managing tools.
Collaboration & Ownership
Nice to Have
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