Senior Machine Learning Engineer (Research Scientist) - Data Foundation & AI

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 Foundation and AI team within Plaid’s Data organization builds and maintains the shared machine learning and AI infrastructure that powers capabilities across Plaid’s product suite. The team transforms Plaid’s unique financial network data into general-purpose representations that can be leveraged by teams across the company. They are responsible for the full lifecycle of these systems, including pretraining data curation, model development and training, as well as production deployment, serving, and ongoing monitoring. As a Senior Research Scientist on the Data Foundation and AI team, you will lead applied research on Plaid’s foundation model by designing model architectures, pretraining objectives, and fine-tuning strategies that generalize across a wide range of downstream product use cases. You will also build and maintain end-to-end production machine learning systems, including training pipelines, model serving infrastructure, feature engineering, and monitoring. In addition, you will develop robust evaluation frameworks to assess model performance across diverse tasks, ensuring quality beyond single-metric optimization.