Staff Machine Learning Engineer (Research Scientist) - DFAI

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. We are the Data Foundation & AI team within Plaid’s Data organization.

Our mission is to build the shared ML and AI infrastructure that powers intelligent capabilities across Plaid’s product suite.

We develop the foundational systems, models, and data assets that transform Plaid’s unique financial network data into scalable, general-purpose representations that teams across the company can leverage.

Our work spans the full ML lifecycle — from large-scale data curation and model pretraining to production serving, evaluation, and monitoring.

As part of the team, you’ll work at the intersection of machine learning infrastructure, applied AI, and distributed systems, helping establish the core AI platform that enables innovation across Plaid.

As a Staff Machine Learning Engineer, you will lead the technical strategy and development of Plaid’s foundation models, driving key decisions across pretraining objectives, model architecture, and fine-tuning approaches that power a wide range of downstream product applications.

You will serve as the technical lead for the full machine learning lifecycle, overseeing everything from data curation and experimentation to production deployment, feature management, and