WeWork is the platform for creators, providing hundreds of thousands of members around the world with space, community and services that enable them to do what they love and create their life's work. Our mission is to create a world where people work to make a life, not just a living, and our own team members are central to that goal.
As a Machine Learning Engineering Intern in the Engagement Mission, you will help to build products that make our members happier and more productive at work. You’ll leverage data that spans across our digital and physical products to help us in creating intelligent environments and connected, consciously-engineered communities in all of our spaces.
As a Machine Learning engineer, your final “output” is working software, and your “audience” for this output often consists of other software components that run autonomously with minimal human supervision. This is why software engineering skills are important to this role.
Over the course of your summer project, you’ll work alongside Machine Learning Engineers to build recommendation systems that will help our members connect with one another both digitally and in the real world. You will build state-of-the art infrastructure, use advanced algorithms and take a large variety of data from a number of sources to intelligently deliver solutions for our member community. You’ll contribute as part of a cross-functional product team consisting of product managers, engineers, data scientists, and designers.
Leverage your skills in software engineering, machine learning, and algorithms to help build one of our core platform products throughout a summer-long project.
Work alongside our Machine Learning Engineers and Data Engineers to determine the right features to use for different recommendation algorithms, and design software components that surface recommendations within our products.
Collaborate with Data Engineers, Data Scientists, and product teams to gain a broad understanding of the breadth of our data and systems to inform the design of your solutions.
Share your insights and recommendations with product and engineering stakeholders to guide the product roadmap and priorities.
Currently pursuing a degree in a quantitative field (e.g., mathematics, computer science, physics, economics, engineering, statistics, operations research, quantitative social science, etc.).
Experience with Python and SQL
Technical understanding of probability & statistics, machine learning (classification, regression, unsupervised, reinforcement, etc.) and optimization algorithms.