Scale AI

Who we Are

Scale’s mission is to accelerate the development of AI by democratizing access to intelligent data. Ultimately, our mission is to build the AWS for AI — data labeling is our first product. Our suite of managed data labeling services combine manual labeling with best in class tools and AI-driven checks to yield stunningly accurate training data. Scale is the standard solution for quality, cost, and scalability and takes the pain out of annotating data and creating high quality datasets. Scale is committed to continual innovation in combining humans with AI to prepare intelligent data, passing on these improvements to our customers and powering a growing future of AI applications.

Our current clients include Alphabet, GM, Pinterest, Uber, Procter & Gamble, Houzz, and many more.

We're backed by world-class investors (Accel, Index), have breakout traction, and are rapidly growing a world-class team. Come join us!

Why join us?

Talented, Dedicated Teammates: Our team (https://www.scaleapi.com/blog/scale-engineering-team) is first-class, hailing from Google, Dropbox, OpenAI, Palantir and more. We push each other constantly and strongly value personal growth, as well as camaraderie and friendship. We ship quality code fast, and we take care of each other.

Compelling Technical Challenges: We're focused on building software and processes to automate and speed up the completion of manual tasks that make AI and ML possible. This space is filled with interesting, difficult technical challenges, and our business must tackle both consumer- and enterprise-like dynamics, not to mention make ever-better use of our increasing mass of data over time.

Strong Growth: We're backed by top-level investors in Accel and Index, and we have very strong product-market fit and traction!

Technical Challenges

1) We're effectively building Amazon Web Services for people. We provide our customers with a statistically guaranteed high quality of service, making sure we're routing incoming tasks to the correct place and assigning the best possible people to each task.

2) We need to build new Machine Learning models to make the process of solving tasks more efficient. We've built products that integrate ML model outputs with human QA and need to continue developing new models as well as building the backend architectures and user interfaces that best combine humans and ML.

3) We have a lot of customer facing software to build, e.g. building more tools into the customer dashboard, improving API design, client libraries, etc.

4) On our backend we have many challenges to improve the routing of tasks to scalers (people completing the incoming tasks), building systems to staff and de-staff scalers based on their quality over time.