Our mission at Scale is to accelerate the development of AI applications.

We’re proud of what we’ve built over the last three years, and today we’re

announcing our Series C funding round to support our continued work against

that mission.

Scale has raised $100m at a valuation of over $1B.

Founders Fund led the round, supported

by Accel,

Mike Krieger and

We are big believers in the transformational impact of AI over the coming

decades. Machine learning is likely the most important technological shift of

our time, and the overall benefits to the world will be comparable to those of

the internet.

What we noticed after working on AI at some of the most advanced organizations

in the world was that building machine learning systems was challenging due to

a lack of mature infrastructure. In particular, we noticed that the critical

bottleneck to further progress today was data—in particular, labeled datasets.

Many engineers will tell you that getting labeled data is the hardest part of

building a machine learning model.

Building AI represents a fundamentally different paradigm than building

traditional software. The performance of an AI system depends more on the data

than the algorithm—in most cases, without labeled data, there is no model.

Similarly, for a model to improve and adapt, it requires more data rather than

simply more code. This paradigm has been true since the very beginning of deep

learning; the modern deep learning age was ignited by the launch of the

ImageNet dataset by Fei-Fei Li’s lab at Stanford in 2009.

We set out to solve this problem by building a platform that enables

organizations with large high-quality datasets needed to build AI systems.

Especially now, as AI is being applied in more and more ways, this matters

because:

Safe, accurate and unbiased AI systems depend on large volumes of high
Safe, accurate and unbiased AI systems depend on large volumes of high
quality training data.
expensive.
Today’s training data bottleneck limits AI's impact to a small group of
The process to acquire, label, and verify training data is slow, manual and
expensive.
prediction, and planning have enabled the development of autonomy, which in
turn relies on data.
Today’s training data bottleneck limits AI's impact to a small group of
well-funded technology companies.
of speech, text, images, and other forms of content.
E-commerce and Search: Powerful search systems that connect

Scale is a solution to these problems. Developers use our API to send their

raw data to our platform. We then use a combination of machine learning and

human insight to label and annotate this data. We pass this high-quality

ground truth data back to our customers, who are then enabled to build

world-class models to solve real-world problems.

Scale is now used by world leading machine learning teams at companies like

OpenAI, Airbnb, Nuro, and Lyft. Our platform is being used to provide

high-quality training data to an ever growing number of machine learning teams

and industries, including:

Autonomous Vehicles: Advancements in AI for perception,
prediction, and planning have enabled the development of autonomy, which in
turn relies on data.
Mapping: Mapping using satellite, drone, and street-level
Natural Language and Content Understanding: More than ever
before, systems can understand and drive decision-making on large corpuses
of speech, text, images, and other forms of content.
be able to understand their environments, safely interact with humans and
E-commerce and Search: Powerful search systems that connect
consumers to specific content, images, or products increasingly rely on AI
systems to drive understanding of this content. Our platform helps engineers
train those systems.
Offline retail: Cashierless checkouts, inventory management
Mapping: Mapping using satellite, drone, and street-level
imagery for use cases from agriculture to insurance rely on high quality
training data to ensure accuracy.
Robotics: Robots in factory or warehouse settings need to
be able to understand their environments, safely interact with humans and
recognize products or components.
Augmented Reality and Virtual Reality: AR and VR systems
used in gaming, real estate or manufacturing rely on being able to perceive
the 3D environment around a camera, headset or user.
Offline retail: Cashierless checkouts, inventory management
systems and footfall analysis all depend on perception systems, which in
turn depend on training data.

We’re proud of the progress we’ve made building the Scale platform for our

customers. It’s been an incredibly quick 3 years since I started the company,

and the ability we’ve had to accelerate progress in machine learning is

palpable. But, as we hope is always the case, we’re far more excited about

what we will do in the future.

There is a lack of mature infrastructure for building AI, forcing teams to

build their own technology stack from scratch before they can have a

meaningful impact. Per our mission, we know there are many problems beyond the

training data bottleneck for us to solve to continue accelerating AI

development. Despite our excitement for the potential of AI, we’re closer to

the beginning of its history than we are to the end.

We’re grateful to our customers for building world-leading AI products with

Scale, and we’re looking forward to accelerating the development of machine

learning in an even broader set of industries and technologies as we grow. If

you are interested in being part of creating the infrastructure machine

learning needs, take a look at our

open positions.

If you're interested in partnering with us, please

reach out.