Scale AI’s Series D: Expanding our Team to Empower AI Dreamers

Today we are excited to share several key updates that build on Scale AI’s mission to accelerate the development of AI.

First, we are officially announcing Scale’s Series D funding round. Following our Series C fundraise last year, Scale has raised $155M at a post-money valuation north of $3.5B led by Tiger Global.

We are in the midst of one of the most impactful technology transformations of human history with AI. The potential applications of AI are going to deeply improve the world: scalable medical diagnosis to everyone in the world, massive reduction in driving accidents, goods which are dramatically cheaper to manufacture and transport, and much more. What’s holding us back from building those dreams is lack of infrastructure. While many organizations are adopting AI, they are forced to build their technology stack from scratch, significantly slowing progress.

Four years ago, we recognized this problem and sought to unlock the bottlenecks holding back AI development. We started by building a platform that solves the necessary, foundational layer that fuels all machine learning models: large annotated datasets. Our solution gives developers the ability to send data to our platform through the Scale API and receive high-quality training data that’s ready for building production-grade AI algorithms. We use a combination of machine learning and human intelligence to annotate tens of millions of data points per week for hundreds of customers like OpenAI, DoorDash, Pinterest, General Motors, NVIDIA, Nuro, and Toyota.

We’ve chosen to stay focused on the applications of AI with the greatest potential for impact: autonomous driving, government, and cutting-edge consumer technology. As AI has transformed every industry, we’ve continued to see demand for our platform to grow—from e-commerce and AR /VR to relevance & personalization and financial services. We are still very early in the journey of AI, and we remain optimistic about all the future dreams we will support.

Our team is tackling the next bottleneck in AI development: managing the full life cycle of AI development across teams. Building and deploying scalable machine learning infrastructure is slow, manual and costly, and in August we launched Nucleus to enable seamless collaboration across the AI development cycle. Teams can visualize and organize their datasets, test and compare models, and share data with colleagues. With Nucleus, our goal is to become the mission control for your data, and over time we’ll be rolling out support for more data types and adding more features to make developing AI easier and faster than ever.