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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.
Job Responsibility:
Build next-generation fraud detection capabilities by researching and prototyping state-of-the-art methods across graph ML, sequential modeling, and multimodal learning
Owning a research roadmap that ships: moving from papers/prototypes to measurable product impact
Publishing applied research and collaborating with a high-caliber team across Data, Product, and Engineering
Working with one of the largest financial datasets to generate insights that help hundreds of millions of consumers achieve greater financial freedom
Requirements:
PhD strongly preferred
we will consider equivalent research experience with a strong publication/innovation track record
3+ years of experience as a Machine Learning Engineer or Research Scientist
Strong scientific rigor and communication
Strong Python skills + ability to build high-quality research prototypes
Nice to have:
Fraud / security / abuse domain experience is a plus
Experience with large-scale training, graph systems, and sequential modeling expertise is a plus