Senior Machine Learning Engineer - Fraud (Research Scientist)

We build simple yet innovative consumer products and developer APIs that shape how everybody interacts with money and the financial system. 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, Washington D.C., London and Amsterdam. The Data team within Plaid’s Fraud organization builds the machine learning systems behind Plaid’s next-generation fraud detection products. Leveraging Plaid’s unique network data, the team develops end-to-end solutions to identify and prevent fraud before it happens. This includes ownership across the full ML lifecycle, from large-scale data processing and model experimentation to feature pipelines, model serving, and ongoing performance monitoring. As a Senior Machine Learning Engineer (Research Scientist) you will lead applied research to develop next-generation fraud detection models across complex data modalities, including relational graphs, sequential events, images, and video. You will design and run rigorous experiments and build evaluation methodologies that reflect real-world fraud dynamics, prototype state-of-the-art architectures such as Graph Neural Networks and Transformer-based foundation models, and partner closely with Machine Learning Engineers to translate successful research into production systems. The role also involves communicating and publishing results internally and externally, helping raise the technical bar for fraud machine learning at Plaid.