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.
Location: Remote
Type: Part-Time Contract (15+ hours per week for 2 weeks | 30 hour minimum commitment)
Duration: 2 weeks with potential for ongoing work
Compensation: Commensurate with experience
You'll evaluate and rate graphic design elements on standardized quality scales to train AI models that assess design effectiveness—contributing to cutting-edge technology that advances how AI understands and evaluates visual content quality.
You thrive on consistency and repetition - You find satisfaction in systematic work and can maintain quality standards while evaluating similar content repeatedly without losing focus or accuracy.
You have a calibrated design eye - You can quickly assess design quality against objective criteria and apply the same standards consistently across hundreds or thousands of examples.
You're objective and principled - You separate personal taste from professional assessment, basing ratings on established design principles rather than subjective preference.
You maintain sustained focus - You can work through high volumes of repetitive tasks while staying sharp and attentive to subtle quality differences.
You're a clear communicator - You flag confusing examples or rating criteria early, ask clarifying questions, and document edge cases clearly.
You're curious about technology - You're genuinely interested in how AI learns to evaluate design quality and see value in contributing to machine learning training data.
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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