PayPal currently has 26 active AI-related job listings. The majority of these roles, 46%, are focused on agents, with serving infrastructure and data roles also representing significant portions. Engineering is the primary function for these hires, with the United States being the dominant hiring country. Frequent tech tags include model_serving, agent_orchestration, and inference_infra. In the last 30 days, PayPal has posted 12 new AI roles, representing a 500% increase compared to the previous 30-day period.
Currently tracking 20 active AI roles, down 49% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $118k–$359k (avg $241k).
PayPal currently has 19 active AI-related roles in our index. The most common open titles are: Sr Machine Learning Engineer (6), Director, Director, ML Engineering & Agentic Systems, Director, Product Management, Lead Product Manager – Risk Decisioning Platform, Machine Learning Engineer. Most positions are in Engineering and Product.
PayPal's active AI hiring is concentrated in: agents (37%), application (26%), data (21%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
PayPal is hiring AI talent in: United States (16 roles), Ireland (3 roles).
Job postings at PayPal most frequently reference: model serving, agent orchestration, recommender systems, inference infra, tool use.
In the past 30 days, PayPal has posted 15 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Sr Data Scientist Senior Data Scientist at PayPal in Chicago, IL, focused on developing and implementing credit risk strategies for PayPal and Venmo credit card programs. The role involves using mathematical, statistical, and data mining methods, including machine learning for model development and deployment, to balance risk and reward, user experience, and financial performance. Responsibilities include automating tracking, testing new data sources, providing insights through monitoring and reporting, and performing operational monitoring. Requires expertise in credit risk management, predictive analytics, experimental design, machine learning, P&L analytics, credit bureau analytics, Python (pandas, numpy, sklearn), SQL (Big Query, Teradata), data visualization tools (Tableau, Amplitude), and database management (Hadoop, Hive, Stampy, Teradata, Big Query). | Post-train | 7 |
| Manager, Venmo BNPL Credit Risk Strategy Manager for Venmo BNPL Credit Risk Strategy, responsible for developing and implementing credit risk strategies, including approve/decline, exposure limits, and APR decisions. This role involves working with the Lending Machine Learning team to leverage or develop credit risk models, collaborating with engineering and product partners, and ensuring adherence to regulatory requirements. |
| Post-train |
| 7 |
| Sr Data Scientist Senior Data Scientist at PayPal in Chicago, IL, focused on applying advanced statistical techniques and machine learning models to predict risk and revenue for consumer lending products. The role involves data mining, building valuation frameworks, optimizing credit portfolio performance, and establishing KPI tracking for credit card program health. Requires experience in forecasting, model development/deployment, predictive analytics, experimental design, P&L analytics, and credit risk management using Python and SQL. | Post-train | 7 |