Senior Machine Learning Engineer (Research Scientist) - DFAI

Plaid•San Francisco, CA

2d•$228,960 - $315,360

About The Position

The Data Foundation and AI team within Plaid’s Data organization builds the shared machine learning and AI infrastructure that powers capabilities across Plaid’s product suite. Our team transforms Plaid’s unique financial network data into scalable, general-purpose representations that can be leveraged by teams throughout the company. We own the full lifecycle of these systems, from pretraining data curation and model development to production serving, evaluation, and monitoring, enabling reliable and impactful AI-driven experiences at scale. As a Senior Research Scientist, you will lead applied research efforts for Plaid’s foundation model, designing model architectures, pretraining objectives, and fine-tuning strategies that generalize across a wide range of downstream product use cases. You will build and maintain production-grade ML systems end-to-end, including training pipelines, model serving infrastructure, feature engineering workflows, and monitoring systems. You will develop robust evaluation frameworks that measure model quality across diverse tasks and real-world applications, rather than optimizing for a single benchmark metric. In close partnership with product and engineering teams across Plaid, you will adapt foundation models to specific business needs and validate their impact through rigorous experimentation. You will also communicate and publish research findings internally and externally, helping advance the state of machine learning and AI across the organization.

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.
3-7 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
Demonstrated ability to ship models to production (not just prototype)
Distributed training experience and strong Python + software engineering fundamentals

Nice To Haves

Fintech / financial data domain experience
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.