Staff Machine Learning Engineer (Research Scientist) - DFAI

Plaid•San Francisco, CA

4d•$249,120 - $367,920

About The Position

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.

Requirements

MS: 7–12+ years of industry experience with a demonstrated track record of technical leadership and production delivery.
PhD: 5–9+ years of industry experience with evidence of technical leadership (tech lead, principal/staff-equivalent roles) and end-to-end production ownership.
Prior technical leadership experience (tech lead, principal, or staff) with demonstrated cross-team influence and mentorship.
Deep expertise in Transformers/LLMs/Foundation Models, including large-scale training or domain adaptation.
End-to-end production ownership; proven track record shipping models through training, serving, monitoring, and iteration in live environments.
Distributed training experience and strong Python + software engineering fundamentals at a staff level.
Ability to drive technical alignment across teams: setting standards, defining integration patterns, and influencing beyond your immediate scope.

Nice To Haves

Fintech / financial data domain experience
External publications or open-source contributions
Experience defining ML platform capabilities (serving infra, feature stores) used across multiple teams.

Responsibilities

Owning the end-to-end technical strategy for a foundation model built on one of the world's richest financial datasets, from pretraining architecture to production serving.
Doing research that ships: driving decisions from experimentation through production systems that serve real customers and power multiple product teams.
Working across the full ML stack, including pretraining objectives, architecture design, distributed training, serving infrastructure, monitoring, and cross-team integration.
Setting technical direction and mentoring a high-caliber team, with your work amplifying the capabilities of engineers and product teams across Plaid.
Helping hundreds of millions of consumers achieve greater financial freedom through the ML capabilities you build and ship.