Sr Machine Learning Engineer

PayPal

Hybrid

Quick summary

Work type: Hybrid
Location: San Jose, CA
Salary: $169,262–$243,500 / yr
Posted: 2 days ago
Closes: Jun 18, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $216k

This role $206k

$158k most similar roles pay here $271k

This role pays less than 58% of similar roles. Most pay $181,397–$249,750 — the shaded band above. At the midpoint, this role pays about $206k versus about $216k for comparable roles.

Based on 240 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 · Sr Machine Learning Engineer

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As a Senior Machine Learning Engineer at PayPal in San Jose, CA, you will join the AI/ML team to tackle complex business challenges, particularly focusing on payment fraud detection. Your daily tasks include analyzing large datasets to extract actionable insights and designing scalable machine learning algorithms that integrate into PayPal’s systems. You will experiment with new models and methodologies to enhance fraud detection capabilities while collaborating with international teams to develop cutting-edge technologies. The role requires expertise in TensorFlow, PyTorch, scikit-learn, and other ML tools, along with a strong foundation in computer science fundamentals, graph-based learning, and cloud computing. With experience in predictive modeling, feature engineering, and model interpretability, you will contribute to maintaining PayPal’s leadership in financial technology by advancing AI/ML technologies that protect users from fraud.

Skills

TensorFlow PyTorch scikit-learn Python graph neural networks cloud computing high-performance computing model interpretability predictive modeling unsupervised learning self-supervised learning pattern recognition representation learning data preprocessing feature engineering statistical validation structured model-training workflows

What you'll do

Develop and deploy scalable machine learning algorithms for fraud detection.
Analyze complex data sets to extract actionable insights for business strategies.
Experiment with innovative models to enhance payment fraud detection capabilities.
Design and manage structured model-training workflows for reproducible results.
Implement graph-based machine learning techniques for improved model performance.

What we're looking for

Doctorate in Computer Science, Engineering, or related field with 1+ year experience.
Expertise in TensorFlow, PyTorch, and scikit-learn for model development.
Proficiency in object-oriented design and Python application development.
Knowledge of graph-based machine learning and neural networks.
Strong background in mathematical foundations of machine learning.
Experience in cloud computing and high-performance computing environments.

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