Startups · AI

Senior Machine Learning Engineer (Research Scientist) - Fraud

Plaid · New York City Office · Remote

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About Plaid

Backed by Index, Kleiner Perkins and NEA.

About the role

Research and prototype state-of-the-art approaches across graph machine learning, sequential modeling, and multimodal learning to build next-generation fraud detection capabilities. Own and execute a research roadmap that translates innovative ideas and prototypes into production solutions with measurable product and customer impact.

What they're looking for

  • 2–4+ years of relevant industry or research lab experience, ideally post-PhD, with demonstrated research leadership and a track record of translating innovative research into measurable product or business impact
  • Demonstrated scientific rigor, with strong written and verbal communication skills and the ability to clearly communicate complex research findings
  • Strong proficiency in Python and experience building high-quality research prototypes that can inform or transition into production systems
More about this role

We believe that 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 and experiences that thousands of developers use 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, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.

We are the Fraud Data team within Plaid's Fraud organization. We leverage relationships and activity across Plaid’s network to develop machine learning models that help customers detect and prevent fraud while minimizing friction for legitimate users. Our team owns the full model lifecycle, from data processing and experimentation to feature pipelines, model serving, and monitoring. In this...

Read the full posting on Plaid's site ↗

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