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 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.
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 |
|---|---|---|
| Lead Product Manager – Risk Decisioning Platform Lead Product Manager for PayPal's Risk Decisioning Platform, focusing on fraud, credit, and compliance. The role involves defining strategy, roadmap, and execution for a platform processing massive signal volumes in milliseconds. Key responsibilities include collaborating with engineering and data science to build scalable solutions, enabling real-time experimentation and rule governance, and driving legacy migration. Experience with scaled platforms like recommendation engines, real-time inference systems, or ML/AI infrastructure is required, with a focus on decisioning, recommendation engines, or real-time scoring. | ServeAgent | 7 |
| Sr Staff Machine Learning Engineer Senior Staff Machine Learning Engineer at PayPal responsible for the strategic direction and execution of machine learning projects, including building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences. |
| Serve |
| 7 |
| Sr Machine Learning Scientist This role focuses on designing, developing, and implementing machine learning models and algorithms for fraud prevention. It involves building scalable ML pipelines, ensuring data quality, and deploying models into production environments, collaborating with cross-functional teams to integrate ML models into products and services, and monitoring their performance. | Serve | 7 |
| Sr Machine Learning Engineer This role focuses on designing, developing, and implementing machine learning models and algorithms, working with data scientists and product teams to enhance services with AI/ML solutions. Responsibilities include building scalable ML pipelines, ensuring data quality, and deploying models into production to drive business insights and improve customer experiences. | Serve | 7 |
| Staff Machine Learning Engineer Staff Machine Learning Engineer at PayPal focused on designing, developing, and implementing advanced ML models and algorithms. The role involves building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences. Requires expertise in ML frameworks and cloud platforms. | Serve | 7 |
| Senior Product Manager – Risk Decisioning Platform Product Manager for PayPal's Risk Decisioning Platform, focusing on real-time fraud, credit, and compliance decisions. The role involves gathering requirements, partnering with engineering and data science, and managing a core platform pillar that processes massive signal volumes in milliseconds. Experience with scaled platforms like recommendation engines or ML inference infrastructure is desired. | Serve | 5 |