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Plaid is seeking a Senior Machine Learning Engineer (Research Scientist) to join the Fraud Data team within the Fraud organization. The role focuses on developing next-generation fraud detection models using machine learning and advanced data science techniques.
You will lead applied research to develop innovative fraud detection models across relational graphs, sequential events, images, and video data. Working closely with Machine Learning Engineers and Data Scientists, you'll design rigorous experiments and evaluation methodologies that reflect real-world fraud dynamics. You'll explore state-of-the-art approaches such as Graph Neural Networks and Transformer-based foundation models, then translate promising research into production-ready solutions.
Key responsibilities include:
- 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
- Publish and share applied research while collaborating with a highly skilled, cross-functional team across Data, Product, and Engineering
- Leverage Plaid's network-level financial data to uncover insights and develop solutions that help hundreds of millions of consumers achieve greater financial freedom
Plaid powers financial connectivity for millions of users and works with thousands of companies including Venmo, SoFi, Fortune 500 companies, and major banks. The company's network covers 12,000 financial institutions across the US, Canada, UK, and Europe.
QUALIFICATIONS:
- PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, or closely related field strongly preferred. Candidates without a PhD may be considered with equivalent research experience, such as significant publications, patents, or widely adopted research contributions in relevant fields
- 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
NICE-TO-HAVE:
- Experience in fraud detection, security, risk, or abuse prevention
- Experience with large-scale training, graph systems, and sequential modeling
