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Plaid // democratizing financial services thru tech
Engineering, Full Time    San Francisco / Remote    Posted: Monday, May 02, 2022
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We believe the way people interact with their finances will drastically improve in the next few years. We're dedicated to empowering this transformation by building the tools & infrastructure developers need to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo & SoFi, several of the Fortune 500, & many of the largest banks to make it easy for people to connect their financial accounts to the apps & services they want to use. Plaid's network covers 11,000 financial institutions across the US, Canada, UK & Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Salt Lake City, Washington D.C., London & Amsterdam.

At Plaid, we believe that the way consumers & businesses interact with their finances will drastically improve in the next few years. Our goal is to build the tools & infrastructure for developers to create this next generation of financial services applications. Today, hundreds of companies such as Venmo, Square, & Coinbase rely on Plaid to integrate with banks & the financial system.

Making data driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct & complete data. We provide tooling & guidance to teams across engineering, product, & business & help them explore our data quickly & safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively. We build the machine learning infrastructure to enable Plaid engineers to prototype & iterate on products & features built on top of consumer-permissioned financial data.

As an engineering manager for the Machine Learning Infrastructure team, you'll lead a rapidly growing team of  8+ engineers. The team works in partnership with a variety of teams such as Data Infrastructure, Product Teams, Infrastructure teams to enable maximum leverage of our data in machine learning use cases & products.
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