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Active1mo ago

Staff Machine Learning Engineer (Research Scientist) - DFAI

Plaid·📍 San Francisco, California, United States

Employment

FULL TIME

Work mode

On-site

Experience

SENIOR LEVEL

Salary

$157,004–$207,000 / year · Jobiba est.

Market pay estimate

$157,004–$207,000 / year for comparable Machine Learning Engineer roles in United States. Not employer-provided.

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Role overview

Job description

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. We are the Data Foundation & AI team within Plaid’s Data organization. Our mission is to build the shared ML and AI in

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Role market pulse

How Machine Learning Engineer demand looks in United States

42/100 · watch

Live jobs

56

Posted 30d

6

30d movement

-88%

Remote share

30.4%

Salary listed

73.2%

Salary trend 1Y

Not enough history

What they are looking for

Skills & requirements

Qualification

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. Fintech / financial data domain

Department · All Departments

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Hiring company

Plaid

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