Sr Machine Learning Engineer

PayPal PayPal · Fintech · Austin, TX +1 · Machine Learning Engineering

This role focuses on evaluating and validating high-impact statistical and AI/ML models, ensuring their integrity, soundness, and robustness in alignment with internal Model Risk Management (MRM) Policy and industry standards. The engineer will provide risk oversight on the deployment of sophisticated machine learning models, build scalable systems, architect end-to-end pipelines for data ingestion, model serving, and monitoring, and integrate deep learning and generative AI methods. The role also emphasizes regulatory compliance and ethical AI use within a fintech domain.

What you'd actually do

  1. Evaluate and validate high-impact statistical and AI/ML models across key business areas.
  2. Perform comprehensive quantitative and qualitative model validation in alignment with internal Model Risk Management (MRM) Policy and industry standards.
  3. Assess model data integrity, design soundness, and performance robustness to identify and report potential risks and deficiencies.
  4. Provide risk oversight on the engineering, automation, and production deployment of sophisticated machine learning models.
  5. Build scalable systems with Python and cloud platforms, and architect end-to-end pipelines for real-time data ingestion, model serving, and monitoring.

Skills

Required

  • designing and implementing scalable machine learning models
  • utilizing deep learning and Generative AI architectures
  • conducting exploratory data analysis (EDA), statistical modeling, and visualization
  • programming in Python for model development, validation, and automation
  • writing and optimizing complex SQL queries
  • developing, validating, and maintaining statistical and AI/ML models for risk management, fraud detection, AML, and compliance
  • performing comprehensive model validation
  • preparing model documentation, validation reports, and findings

Nice to have

  • collaboration with DevOps, IT, and business partners
  • integration of cutting-edge methods in deep learning and generative AI
  • mentoring junior engineers
  • leading technical projects
  • coordinating project roadmaps

What the JD emphasized

  • Model Risk Management (MRM) Policy
  • regulatory compliance
  • ethical AI use

Other signals

  • model validation
  • risk management
  • MLOps
  • generative AI