Staff Machine Learning Engineer (Research Scientist) - DFAI

About the Role

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 foundational systems, models, and data assets spanning the full ML lifecycle — from large-scale data curation and model pretraining to production serving, evaluation, and monitoring.

As a Staff Machine Learning Engineer, you will lead the technical strategy and development of Plaid’s foundation models, driving key decisions across pretraining objectives, model architecture, and fine-tuning approaches. You will serve as the technical lead across the ML lifecycle, establish evaluation frameworks, build scalable pipelines, partner across teams, and mentor engineers.

Responsibilities

• Owning the end-to-end technical strategy for a foundation model built on financial datasets, from pretraining architecture to production serving.
• Doing research that ships: driving decisions from experimentation through production systems that serve real customers.
• 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.
• Helping hundreds of millions of consumers achieve greater financial freedom through the ML capabilities you build and ship.

Qualifications

Required

• MS with 7–12+ years of industry experience, or PhD with 5–9+ years of industry experience with demonstrated technical leadership and end-to-end production delivery.
• 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.
• Proven track record shipping models through training, serving, monitoring, and iteration in live environments.
• Distributed training experience and strong Python and software engineering fundamentals at a staff level.
• Ability to drive technical alignment across teams: setting standards, defining integration patterns, and influencing beyond immediate scope.

Nice to Have

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