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Job DescriptionWe 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, Washington D.C., London and Amsterdam.We’re the Data team within Plaid’s Fraud organization, and we’re on a mission to stop fraud before it happens. Our team builds the machine learning systems that power Plaid’s most advanced fraud detection products, harnessing the scale and richness of Plaid’s network data to protect consumers and businesses alike. We own the full ML lifecycle — from feature pipelines and model training to deployment and monitoring — ensuring our systems are reliable, scalable, and ready to support hundreds of customers as Plaid continues to grow.As a Machine Learning Engineer on Plaid’s Fraud Data team, you’ll play a key role in shaping the future of fraud prevention. You’ll develop new features and machine learning models that enhance the accuracy and effectiveness of our fraud detection systems, while building reliable data and model pipelines to power both experimentation and production workflows. Working closely with data science, infrastructure, and product teams, you’ll help design and deliver scalable, high-quality ML systems that protect Plaid’s customers at scale. You’ll also have the opportunity to explore and prototype GenAI-driven capabilities that push the boundaries of our fraud modeling and investigation tools.***We are open to remote candidates for this role***
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