Work Styles at Zoom
In most cases, you will have the opportunity to choose your preferred working location from the following options when you join Zoom: in-person, hybrid or remote. Visit this page for more information about Zoom's Workstyles.
About Us
Zoomies help people stay connected so they can get more done together. We set out to build the best video product for the enterprise, and today help people communicate better with products like Zoom Contact Center, Zoom Phone, Zoom Events, Zoom Apps, Zoom Rooms, and Zoom Webinar.
We’re problem-solvers, working at a fast pace to design solutions with our customers and users in mind. Here, you’ll work across teams to deliver impactful projects that are changing the way people communicate and enjoy opportunities to advance your career in a diverse, inclusive environment.
You will be part of a team whose focus is to solve cutting edge AI / MLproblems and deploy models that constantly advance the state-of-the-art. You will be working on designing the data infrastructure and solve other interesting bottlenecks that are challenging at Zoom's scale.
Responsibilities :
Design and implementation of data infrastructure for Machine Learning (ML) projects for our Research and Development Org
Assembling large, complex data sets and make the data more discoverable and easy to be used by the AI / ML team to support various Machine Learning initiatives. This includes unifying pre-processing work flows for data for different AI tasks.
Build and manage data sets, also by identifying and using suitable big data technologies, such as Apache Hadoop, DVC, Apache Drill,
Write tools to transform raw data sources into easily accessible models by coding across several languages such as Python, and SQL
Deploy, develop and maintain tools for data collection, data crawling and data annotation, e.g. as web applications using Django and Angular or similar technology.
Along with looking at external sources of data, build data expertise and own data quality for the pipelines you create.
Requirements :
Bachelor’s or higher degree in Computer Science or related fields
Experience in building and optimising data pipelines
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