Releases

Scale announced a restructuring that reduced its employee workforce by roughly 20 percent after expanding too quickly during the prior growth cycle.

Scale launched Outlier to connect subject-matter experts with generative-AI training and evaluation projects.

Scale expanded its data engine beyond perception data into expert demonstrations, preference feedback, red teaming, and reinforcement learning from human feedback for language models.

The U.S. government awarded Scale a contract vehicle worth up to $249 million to make its AI data and development capabilities available across federal agencies.

Scale acquired Berlin-based SiaSearch and folded its petabyte-scale sensor-data search technology and team into Scale Nucleus.

Scale moved Rapid into general availability for fast labeling experiments without data minimums.

Scale established a dedicated St. Louis operations center to develop skilled U.S. data and evaluation workforces for government programs.

Scale raised $325 million in Series E funding co-led by Dragoneer, Greenoaks, and Tiger Global at a reported $7.3 billion valuation.

Scale acquired real-time video-understanding startup Helia AI and brought its team and infrastructure expertise into the Nucleus platform.

Scale raised $155 million in a Series D led by Tiger Global at a post-money valuation above $3.5 billion.

Scale released the core Nucleus workspace for dataset visualization, search, curation, collaboration, and model-error analysis.

Scale launched Nucleus as a dataset-management and model-debugging workspace for visualizing, curating, searching, and collaborating on machine-learning data.

Scale introduced a self-serve, pay-as-you-go product for receiving production-quality labels and instruction feedback in as little as one hour.

Scale launched a mapping product for computer-vision and autonomous path-planning workflows.

Scale began supporting U.S. defense and national-security AI programs, extending its data infrastructure into government missions.

Scale raised $100 million in Series C funding led by Founders Fund at a valuation above $1 billion.

Scale introduced a managed workspace for configuring annotation projects, instructions, quality controls, and review workflows.

Scale expanded its annotation platform around the image, video, lidar, and sensor-fusion requirements of autonomous-vehicle developers.

Scale raised $18 million in Series B funding led by Index Ventures with participation from Accel, Y Combinator, and other investors.

Scale raised $4.5 million in Series A funding led by Accel to expand its labeled-data infrastructure.

Scale began building distributed contributor platforms and training operations to support large-scale annotation, expert-data, and evaluation programs.

Scale launched Remotasks to train and coordinate distributed contributors performing data annotation and review tasks.

Scale launched the data infrastructure that became its core Data Engine, combining APIs, machine assistance, and human review to create high-quality training data.

Scale launched an API that let developers submit human-powered data tasks with one line of code, establishing the foundation of its AI data business.

Alexandr Wang and Lucy Guo founded Scale in San Francisco and entered Y Combinator to build an API for routing human work, which evolved into AI data infrastructure.