Staff Machine Learning Engineer (Research Scientist) - DFAI

Plaid is a financial data network and fintech infrastructure company that helps people securely connect financial accounts to digital financial services.

Series D0 current maintainers0 active leadsTeam intelligence

Maintainer signals as of 9/2/2026

San Francisco, United States

Funding history

About Plaid Inc.

Plaid provides developer infrastructure and financial tools for account connectivity, financial data access, bank payments, identity verification, AML monitoring, credit and underwriting, and fraud prevention. Its network supports thousands of fintech companies and more than 12,000 financial institutions across the United States, Canada, the United Kingdom, and Europe.

View jobs by Plaid Inc.

Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

Lead the technical strategy and development of Plaid's foundation models across pretraining, architecture, fine-tuning, data curation, experimentation, production deployment, feature management, evaluation, and observability. Partner across teams to build reusable ML infrastructure and communicate technical advancements.

Requirements

  • MS with 7–12+ years of industry experience in technical leadership and production delivery, or PhD with 5–9+ years and evidence of technical leadership and production ownership.
  • Prior technical leadership experience as a tech lead, principal, or staff engineer, with cross-team influence and mentorship.
  • Deep expertise in Transformers, LLMs, and foundation models, including large-scale training or domain adaptation.
  • End-to-end ownership of models through training, serving, monitoring, and iteration in live environments.
  • Distributed training experience and strong Python and staff-level software engineering fundamentals.
  • Ability to drive technical alignment, set standards, define integration patterns, and influence beyond an immediate team.
  • Fintech or financial data experience is nice to have.
  • External publications or open-source contributions are nice to have.
  • Experience defining ML platform capabilities such as serving infrastructure and feature stores is nice to have.

Responsibilities

  • Own end-to-end technical strategy for a foundation model, from pretraining architecture through production serving.
  • Drive research decisions from experimentation into production systems serving customers and product teams.
  • Work across pretraining, architecture design, distributed training, serving infrastructure, monitoring, and cross-team integration.
  • Set technical direction and mentor engineers and product teams across Plaid.
  • Build and ship ML capabilities that support financial freedom for consumers.

Benefits

  • Medical, dental, vision, and 401(k)
  • Additional compensation may include equity and/or commission.