Company Background

WeWork is the platform for creators, providing hundreds of thousands of members across the globe space, community, and services that enable them to do what they love and craft their life's work. Our mission is to build a world where people work to make a life, not just a living, and our own team members are central to that goal.

WeWork manages hundreds of buildings and serves hundreds of thousands of members around the world. Workspace intelligence is critical for building a community that enhances productivity, encourages innovation, and strengthens collaboration.

 Workspace Intelligence relies on data — how to gather it, how to analyze it, and what to do with the knowledge derived from it. We invest in areas such as pervasive computing, data management, machine learning, and computational social science to understand the environment, the people, as well as how they interact with others. Furthermore, we also analyze data in the digital world: We uncover social dynamics from digital communication including emails, messages and social networks.

Role

The platform engineering team is looking for a full-stack software engineer to buildout machine learning platform tooling for scientists and researchers to use. The components include feature visualization, modeling training monitoring, logs visualization, version control & model deployment workflow. Candidate should have good time management and multitasking skills.

Requirements

  • Implementing libraries to improve reusability of code between software and analysis tools
  • Enhancing our CI pipelines to improve build-throughput and provide a more useful error reporting
  • Optimizing our software deploy process to better support pushing releases to remote data centers
  • Demonstrated full knowledge of GitHub
  • Experience using and configuring continuous integration/continuous delivery pipelines 
  • Actively engage with Engineering, Support and Publications organizations as the subject matter expert for application integration.
  • Develop tools for collecting and processing data for defect analysis and performance measurement.
  • Use creative and efficient methodologies to guarantee continuous tracking of the software project stability, automatic delivery and deployments of project artifacts, and efficient infrastructure supporting both operations and development teams
  • Focus on activities related to building and maintaining automation workflows for building, packaging, and deploying of SaaS software.
  • Programming languages: Python, React.js, Ruby on Rails, Java

Frameworks & tools

Node.js, D3.js, Tensorboard, Kubernetes, Docker, Helm

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