Manager, Machine Learning Engineering
Job Description
Senior Machine Learning / AI Engineer – PayPal
Company: PayPal
Team: Data Science – Risk Management
Focus: Machine Learning, AI, Fraud Risk Management
Experience: 8+ years
Work Model: Balanced hybrid — generally 3 days office + 2 days office/home
Role Overview
The role focuses on designing, developing and deploying advanced machine learning models and scalable ML systems for fraud-risk management. You will work with data scientists, software engineers, product teams and business stakeholders to automate operational reviews, reduce fraud losses and improve customer experience.
Key Responsibilities
Design, develop and optimize ML models for complex business problems.
Process and analyze large datasets to generate actionable insights.
Build scalable ML pipelines and ensure data quality.
Deploy ML models into production environments.
Integrate ML solutions into products and services.
Monitor and evaluate production model performance.
Work with stakeholders to understand changing business requirements and drive adoption.
Contribute to solutions balancing innovation, regulatory compliance, fraud prevention and customer experience.
People Management
Oversee team capabilities, delivery and execution.
Mentor and guide team members.
Support career development and growth.
Lead teams through ML projects and delivery.
Required Qualifications
8+ years of relevant professional experience.
Bachelor’s degree or equivalent experience.
Strong experience designing, implementing and deploying ML models.
Experience scaling machine-learning systems.
Expertise with at least one major ML framework:
TensorFlow
PyTorch
Scikit-learn
Familiarity with AWS, Azure or GCP.
Experience with data processing and ML model deployment tools.
Best-Fit Profile
This is particularly suited to an experienced ML Engineer / Senior ML Engineer / ML Lead with production ML experience and exposure to risk, fraud, financial services or large-scale decisioning systems. Leadership and stakeholder-management experience is important for the people-management component.
