Staff Machine Learning Engineer (research Scientist) - Dfai

Plaid Plaid · Fintech · San Francisco, CA · All Departments

Staff Machine Learning Engineer (Research Scientist) at Plaid, focusing on the Data Foundation & AI team. The role involves leading technical strategy and development of foundation models, covering the full ML lifecycle from pretraining to production serving, evaluation, and monitoring. The position requires expertise in Transformers/LLMs, large-scale training, and distributed training, with a focus on shipping models that power product applications and mentoring other engineers.

What you'd actually do

  1. 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.
  2. Doing research that ships: driving decisions from experimentation through production systems that serve real customers and power multiple product teams.
  3. Working across the full ML stack, including pretraining objectives, architecture design, distributed training, serving infrastructure, monitoring, and cross-team integration.
  4. Setting technical direction and mentoring a high-caliber team, with your work amplifying the capabilities of engineers and product teams across Plaid.
  5. Helping hundreds of millions of consumers achieve greater financial freedom through the ML capabilities you build and ship.

Skills

Required

  • 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 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.

What the JD emphasized

  • end-to-end technical strategy
  • pretraining architecture
  • production serving
  • full ML lifecycle
  • pretraining objectives
  • architecture design
  • distributed training
  • serving infrastructure
  • monitoring
  • cross-team integration
  • technical leadership
  • production delivery
  • end-to-end production ownership
  • shipping models through training, serving, monitoring, and iteration in live environments
  • distributed training experience
  • staff level
  • drive technical alignment

Other signals

  • foundation models
  • pretraining
  • production serving
  • evaluation frameworks
  • technical strategy