Machine Learning Engineer

PayPal

Hybrid

Quick summary

Work type: Hybrid
Location: Chicago, Illinois · Austin, Texas
Salary: $117,500–$174,350 / yr
Posted: 90 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $216k

This role $146k

$100k most similar roles pay here $280k

This role pays less than 94% of similar roles. Most pay $181,587–$249,750 — the shaded band above. At the midpoint, this role pays about $146k versus about $216k for comparable roles.

Based on 239 similar postings.

Employer

About PayPal

PayPal is a leading global digital wallet and online payment system, founded in 1998, that allows individuals and businesses to send, receive, and manage funds securely in over 200 markets.

PayPal currently has 84 open roles on FindRole.

Listed pay typically runs $160,500–$235,826 across 84 roles with salary data.

Most-posted roles

View all roles at PayPal

At a glance

TL;DR · Machine Learning Engineer

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As a mid-level machine learning engineer at PayPal, you will collaborate with senior engineers and data scientists to develop and optimize AI models for various business functions such as fraud detection and credit underwriting. Your day-to-day responsibilities include preprocessing datasets, conducting experiments, and ensuring model compliance with regulatory standards like Responsible AI. You will need advanced knowledge of ML frameworks like TensorFlow and scikit-learn, along with expertise in Python, SQL, Hadoop, and Spark. The role requires an advanced degree in a quantitative field and experience in credit scoring or financial forecasting, alongside strong analytical skills and the ability to communicate effectively with diverse stakeholders.

Skills

Python TensorFlow scikit-learn Hadoop Spark SQL logistic_regression time_series_analysis random_forests SVMs XGBoost CNNs RNNs CI/CD

What you'll do

Develop and optimize machine learning models to solve complex business problems.
Conduct quantitative model validation to identify and mitigate risk issues.
Collaborate with business units to remediate model issues and improve AI applications.
Ensure compliance of AI applications with evolving regulatory expectations for Responsible AI.
Stay updated on advancements in statistical and machine learning models.

What we're looking for

1+ year of relevant experience in machine learning or a related field.
Bachelor’s degree in quantitative fields like statistics, mathematics, computer science, or engineering.
Proficiency with ML frameworks such as TensorFlow and scikit-learn.
Advanced knowledge of statistical and machine learning models including logistic regression, SVMs, XGBoost.
Strong coding skills in Python, SQL, Hadoop, Spark for big data processing.
Experience in credit scoring, fraud detection, financial forecasting, or marketing analytics.
Excellent communication and collaboration skills to work with diverse stakeholders.

Hybrid

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