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

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.

 

 

The target base salary for this position ranges from $228,960/year to $315,360/year in Zone 1.

Zone 1 - San Francisco / New York City / Seattle

The base salary range listed for this full-time position excludes commission (if applicable), equity and benefits. The pay range shown on each job posting is the minimum and maximum target for new-hire salaries. Actual pay may be higher or lower depending on factors like skills, experience, and relevant education or training.

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.

Strong applied ML research skills with production delivery experience.

Depth in Transformers/LLMs, representation learning, or large-scale model training.

Demonstrated ability to ship models to production (not just prototype).

Distributed training experience and strong Python + software engineering fundamentals.

Fintech / financial data domain experience is a plus.

External publications or open-source contributions is a plus.