About the job
The future of artificial intelligence — whether in training or evaluation, classical machine learning or agentic workflows — begins with exceptional data.
At HumanSignal, we are developing a pioneering platform that facilitates the creation, curation, and assessment of this critical data. Our tools enable leading AI teams to fine-tune foundational models and validate agent behaviors in production, ensuring that models are based on real-world signals rather than mere noise.
Our open-source product, Label Studio, has emerged as the de facto standard for data labeling and evaluation across various modalities including text, images, time series, and agents-in-environments. With over 250,000 users and hundreds of millions of labeled samples, it is the most widely adopted open-source solution for teams engaged in building AI systems.
Label Studio Enterprise enhances this momentum by providing the security, collaboration, and scalability features essential for supporting mission-critical AI pipelines — from model training datasets to evaluation test sets and continuous feedback loops. We began our journey before foundational models became mainstream, and we are intensifying our efforts now that AI is transforming the world. If you're passionate about assisting leading AI teams in building smarter, more precise systems, we want to hear from you.
About Us
HumanSignal develops Label Studio Enterprise, the trusted data annotation platform utilized by Fortune 500 companies like Apple. We are expanding our team at our Columbus facility, which currently has over 400 members, and we are seeking engineering talent to help create systems that power AI training pipelines.
What You’ll Build
- Full-stack web applications for crowdsourced data collection with integrated quality control workflows
- Admin dashboards featuring bulk operations, analytics, and workflow management
- Data pipelines that integrate Label Studio, AWS S3, and automated quality checks
- JSON transformation systems that handle thousands of annotation files for enterprise clients

