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Active1w ago

Senior Machine Learning Engineer - Fraud

Plaid·📍 San Francisco, California, United States

Employment

FULL TIME

Work mode

Remote

Experience

7+ years

Salary

$189,308–$292,000 / year · Jobiba est.

Market pay estimate

$189,308–$292,000 / year for comparable Machine Learning Engineer roles in United States. Not employer-provided.

Salary →

Role market pulse

How Machine Learning Engineer demand looks in United States

46/100 · steady

Live jobs

48

Posted 30d

10

30d movement

-73.7%

Remote share

27.1%

Salary listed

77.1%

Salary trend 1Y

Not enough history

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Fraud Data team at Plaid builds the machine learning systems that power Plaid’s fraud detection

What they are looking for

Skills & requirements

pythonsqlawsgitmachine learningaigofinancehrtraining

Qualification

7+ years of professional experience in machine learning, applied science, or software engineering for ML, including hands-on model development and deployment. Hands-on experience designing, training, tuning, and deploying models, and measuring improvements in production performance or business metrics. Strong ML and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing why a model underperforms. Strong understanding of the strengths, limitations, and applications for both traditional and modern ML methods, including gradient-boosted trees and neural networks. Experience constructing training datasets and addressing label quality, data leakage, class imbalance, and generalization across time periods or populations. Strong Python skills, SQL proficiency for working with training and evaluation data, and hands-on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents. Experience independently leading ML projects from an open-ended problem through deployment, coordinating requirements and model releases with Data Science, Product, and Engineering. Nice-to-Have: Strongly preferred: Fraud or risk modelin

Department · All Departments

P

Hiring company

Plaid

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