Machine Learning Engineer (Research Scientist) - DFAI

Plaid•San Francisco, CA

About The Position

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.

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

Nice To Haves

Fintech / financial data domain experience
Demonstrated ability to ship models to production (not just prototype)
External publications or open-source contributions

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.