Staff Machine Learning Engineer (Research Scientist) - DFAI

Plaid

Quick summary

Work type
On-site
Location
San Francisco, CANew York, NYSeattle, WA
Salary
$249,120–$367,920 / yr
Posted
25 days ago
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $230k
This role $309k
$159k most similar roles pay here $390k

This role pays more than 93% of similar roles. Most pay $198,800–$260,899 — the shaded band above. At the midpoint, this role pays about $309k versus about $230k for comparable roles.

Based on 240 similar postings.

Employer

About Plaid

Plaid is a financial technology company that builds a data network powering digital finance applications, enabling consumers to securely connect their financial accounts to apps and services. Industry: Financial Technology & Data Infrastructure

Plaid currently has 98 open roles on FindRole.

Listed pay typically runs $190,800–$262,800 across 98 roles with salary data.

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At a glance

TL;DR · Staff Machine Learning Engineer (Research Scientist) - DFAI

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As a Staff Machine Learning Engineer on Plaid’s Data Foundation & AI team, you will lead the technical strategy and development of foundational models built on one of the world's richest financial datasets. Your responsibilities include driving decisions from pretraining objectives to production deployment, working across the full ML stack including distributed training and serving infrastructure, and setting technical direction while mentoring a high-caliber team. You will establish rigorous evaluation frameworks and build scalable pipelines that translate research into production impact, partnering closely with cross-functional teams to define how products integrate with foundation models. This role requires deep expertise in Transformers/LLMs/Foundation Models, end-to-end production ownership, and strong Python skills, as well as the ability to drive technical alignment across teams and influence beyond your immediate scope.

Skills

Python Transformers LLMs Foundation Models Distributed Training CI/CD MLOps Feature Stores Monitoring Serving Infrastructure Kubernetes AWS PostgreSQL

What you'll do

  • Own the end-to-end technical strategy for foundation models, from pretraining to production.
  • Drive decisions on model architecture and fine-tuning approaches that power product applications.
  • Oversee data curation, experimentation, deployment, feature management, and observability in ML lifecycle.
  • Establish evaluation frameworks to measure model performance across diverse use cases.
  • Mentor engineers and set rigorous standards for engineering and experimentation practices.
  • Partner with cross-functional teams to define integration patterns for foundation models.

What we're looking for

  • MS or PhD with significant industry experience and proven technical leadership.
  • Deep expertise in Transformers, LLMs, Foundation Models, and large-scale training.
  • Proven track record of end-to-end production ownership for shipped models.
  • Strong Python skills and distributed training experience at a staff level.
  • Ability to drive technical alignment and set standards across multiple teams.

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