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Data Engine

Collect, curate, and annotate data. Train models and evaluate. Repeat.


The Best In The Business

The Scale Data Engine is trusted by the world’s leading ML teams to accelerate the development of their models. The scale of our operations, experts and quality is unmatched in the industry.


Scale can provide the core tenet of any dataset with high-quality labels from domain experts.

Cost Effective

Easily find, categorize, and fix model failures with Scale’s Data Engine. Then, optimize labeling spend with high-value curated data.


Scale's data engine can support any ML project from lower-volume experiments to high-volume production projects. Scale up, or down, as needed.


Scale delivers the greatest variety and diversity of data to help deliver the greatest value to your model performance.

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Scale Data Engine

For AI teams, Scale Data Engine improves your models by improving your data.


Powering the next generation of Generative AI

Scale Generative AI Data Engine powers the most advanced LLMs and generative models in the world through world-class RLHF, data generation, model evaluation, safety, and alignment.

AI Text Generator

Data Labeling

The best quality data to fuel the best performing models

Scale has pioneered in the data labeling industry by combining AI-based techniques with human-in-the-loop, delivering labeled data at unprecedented quality, scalability, and efficiency.

Data Curation

Unearth the most valuable data by intelligently managing your dataset

Scale’s suite of dataset management, testing, model evaluation, and model comparison tools enable you to “label what matters.” Maximize the value of your labeling budget by identifying the highest value data to label, even without ground truth labels.


The One-Stop-Shop For Building AI

Data engine is the process of improving machine learning models with high quality, diverse and large datasets powered by experts. Unlock model performance with the Scale Data Engine.

Generative AI Data Engine


After initial pre-training, create complex prompt-response pairs from scratch.


Apply human preferences to model outputs.

Red Teaming

Use prompt injection techniques to find vulnerabilities.


Evaluate your model against a set of complex and diverse prompts to find weak points.


Supported Annotation Types

Scale Text

Document Processing
Natural Language Processing
Content & Language

Scale Image

Electro Optical

Scale Video

Full Motion Video
Natural Language Processing

Scale Audio

Active & Passive Sonar

Scale 3D Sensor Fusion

One of the things we love about Scale is the fact that we can fully label the world. We can label 2D bounding boxes, 3D bounding boxes, but also semantic segmentation, including in 3D, to understand as much as possible, including scenarios we don’t foresee today.

Adrien Gaidon

Machine Learning Lead, Toyota Research Institute

Scale has made it easier for us to gather annotations at a good price point. The UI is simple to navigate, and the built in worker evaluation pipeline and batch options saves us time and helps enforce best practices so that we can get high-quality training data.

Cassandra Ung

Software Engineer, Square

“ML models only deliver the highest accuracy when they can handle edge cases that might be challenging, uncommon, or even dangerous. The Autotag functionality in Data Engine: Dataset Management helps us immensely by identifying examples of infrequent scenarios in our dataset, all with a simple query. As Nuro works to ensure efficient deliveries as safely as possible, we depend on tools like Scale Data Engine: Dataset Management to curate edge cases which we can use to train ever more accurate and capable models.”

Jack Guo

Head of Autonomy Platform, Nuro

“After training for years to do this research, it was frustrating how much time I was spending just annotating data. Working with Scale Rapid freed up my time to work on the parts of research that require my expertise.”

Caleb Weinreb

Neuroscience Post-Doc, Harvard Medical School

Scale already provided quality annotations to our perception team, so it was a natural extension to use their platform and solve adjacent pipeline problems of data selection and model performance debugging. The powerful search capabilities and easy-to-use tools made it easy for us to get started with our existing library of annotations.

Oliver Monson

Sr. Manager, Data Operations, Velodyne LiDAR

The future of your industry starts here.

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