Senior Machine Learning Engineer (Research Scientist) - Fraud
Plaid
- Pay
- $229k–$315k
- Workplace
- Hybrid
- Location
- New York City Office · New York · United States
- First seen
- 1 hour ago
- Last seen
- 1 hour ago
- Board
- Ashby
Summary
We believe that the way people interact with their finances will drastically improve in the next few years.
Posting
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 research role, you’ll partner closely with Machine Learning Engineers and Data Scientists to uncover new signals, explore and evaluate innovative modeling approaches, and translate promising research into production-ready solutions that strengthen fraud detection across Plaid’s network. As a Senior Research Scientist, you will lead applied research to develop next-generation fraud detection models across relational graphs, sequential events, images, and video data. You’ll design rigorous experiments and evaluation methodologies that reflect real-world fraud dynamics, while exploring state-of-the-art approaches such as Graph Neural Networks and Transformer-based foundation models. In close partnership with Machine Learning Engineers, you’ll translate promising research into production-ready solutions and communicate your findings internally and externally to advance the technical bar for fraud machine learning at Plaid. Responsibilities: - 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. - Pu