Machine Learning Engineer (Research Scientist) - DFAI

Responsibilities

• Building a foundation model on one of the world's richest financial datasets that no one else has.
• Doing research that ships: moving from experimentation and prototypes to production systems serving real customers.
• Working across the full ML stack, from pretraining objectives and architectures to serving infrastructure and monitoring.
• Collaborating with a high-caliber team and seeing your work amplify the capabilities of multiple product teams.
• Helping hundreds of millions of consumers achieve greater financial freedom through data-driven products.

Requirements

• MS or PhD in ML/AI/CS/Stats/Applied Math (or closely related). PhD preferred but not required - candidates with equivalent industry research and production experience will be considered.
• 1-3 years of industry experience building and deploying ML models, with evidence of both research depth and production delivery
• Strong applied ML research skills with production delivery experience
• Depth in Transformers/LLMs, representation learning, or large-scale model training
• Distributed training experience and strong Python + software engineering fundamentals
• Fintech / financial data domain experience - Nice to have
• Demonstrated ability to ship models to production (not just prototype) - Nice to have
• External publications or open-source contributions - Nice to have

Benefits

Additional Information

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 & AI team sits within Plaid's Data organization and is responsible for building the shared machine learning and AI infrastructure that powers innovation across Plaid's product portfolio. The team transforms Plaid's unique financial network data into scalable, general-purpose representations that can be leveraged by teams throughout the company. Working across the full model lifecycle, the team develops and curates pretraining datasets, trains and evaluates foundation models, and operates production-grade serving and monitoring systems to ensure reliable, high-impact AI capabilities at scale. As a Research Scientist, you will advance Plaid's foundation models by developing novel model architectures, pretraining objectives, and fine-tuning strategies that generalize across a broad range of financial and product applications. You will contribute to the design, development, and operation of production machine learning systems, working across the full stack from data and feature engineering to training pipelines, model serving, and monitoring. The role also involves creating comprehensive evaluation frameworks that assess model performance across diverse tasks and use cases, ensuring robust and reliable outcomes. In close partnership with product and engineering teams, you will adapt foundation models to solve specific business challenges, validate their impact through rigorous experimentation, and translate research advances into production capabilities. You will also share insights and results with both internal and external audiences, helping drive innovation and elevate the practice of machine learning and AI across Plaid.

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