Sr Machine Learning Engineer

PayPal

Hybrid

Quick summary

Work type: Hybrid
Location: Chicago, Illinois · San Jose, California · Austin, Texas
Salary: $145,000–$215,050 / 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 $180k

$131k most similar roles pay here $274k

This role pays less than 75% of similar roles. Most pay $181,855–$249,750 — the shaded band above. At the midpoint, this role pays about $180k 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

Apply Now Log in to save

This position involves independently validating and overseeing high-impact statistical and machine learning models in critical business areas like credit risk scoring, fraud detection, marketing analytics, and collections. The role requires assessing model soundness, data quality, and performance to mitigate risks while collaborating with developers and business partners on improvements. Responsibilities include supporting AI and model risk governance to ensure compliance with PayPal’s enterprise risk framework and regulatory standards. Ideal candidates hold advanced degrees in quantitative fields and possess strong knowledge of statistical techniques and machine learning algorithms such as logistic regression, random forests, and deep learning architectures. Proficiency in Python (Scikit-learn, TensorFlow), SQL, Hadoop, and Spark is essential, along with experience in relevant domains like credit risk or fraud detection. Strong collaboration and communication skills are crucial for articulating technical concepts to non-technical stakeholders.

Skills

Python Scikit-learn TensorFlow SQL Hadoop Spark logistic_regression time_series_modeling random_forests support_vector_machines XGBoost CNNs RNNs model_governance SR_11-7 OCC_2011-12 EU_AI_Act

What you'll do

Independently validate and oversee high-impact statistical and machine learning models.
Assess model soundness, data quality, and performance to mitigate risk.
Collaborate with developers on remediation strategies for identified issues.
Ensure compliance with PayPal’s enterprise risk framework and regulatory standards.
Communicate complex technical concepts clearly to non-technical stakeholders.

What we're looking for

Advanced degree in a quantitative field like Statistics, Mathematics, Computer Science, Engineering.
Expertise in statistical and machine learning techniques including logistic regression, random forests, SVMs, XGBoost, CNNs, RNNs.
Proficiency with programming tools such as Python (Scikit-learn, TensorFlow), SQL, Hadoop, Spark for big data analysis.
Relevant experience in credit risk scoring, fraud detection, financial forecasting, or marketing analytics.
Strong collaboration and communication skills to work effectively both independently and within a team.
Ability to articulate complex technical concepts clearly to non-technical stakeholders.