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Labelbox is building software infrastructure for industrial data science teams to do data labeling for the training of neural networks. When we build software, we take for granted the existence of collaborative tools to write and debug code. The machine learning workflow has no standard tooling for labeling data, storing it, debugging models and then continually improving model accuracy. Enter Labelbox. Labelbox's vision is to become the default software for data scientists to manage data and train neural networks in the same way that GitHub or text editors are defaults for software engineers.





Current Labelbox customers include American Family Insurance, Lytx, Airbus, Genius Sports, Keeptruckin and more. Labelbox is venture backed by Google, Kleiner Perkins and First Round Capital and has been featured in Tech Crunch Web Summit and Forbes



Responsibilities





Strong understanding of Javascript with an interest in using Typescript

Build component driven applications with ReactJS

Experience working with Redux and architecting large single page applications

Experience and interest in frontend testing



Follow-on Responsibilities





Using canvas to build highly performant labeling interfaces

Building new features and resolvers in our GraphQL API with Node.JS

Experience with SQL databases

Experience optimizing web traffic



Requirements





2 years of experience building data rich frontend web applications

A bachelor’s degree (or equivalent) in computer science or a related field.

We believe that AI has the power to transform every aspect of our lives -- from healthcare to agriculture. The exponential impact of artificial intelligence will mean mammograms can happen quickly and cheaply irrespective of the limited number of radiologists there are in the world and growers will know the instant that disease hits their farm without even being there.





At Labelbox, we’re building a platform to accelerate the development of this future. Rather than requiring companies to create their own expensive and incomplete homegrown tools, we’ve created a training data platform that acts as a central hub for humans to interface with AI. When humans have better ways to input and manage data, machines have better ways to learn.