Principal Machine Learning Engineer

PayPal PayPal · Fintech · San Jose, CA +1 · Machine Learning Engineering

Principal Machine Learning Engineer at PayPal focused on driving strategic vision and development of ML models and algorithms to solve complex problems, enhance services, build scalable ML pipelines, and deploy models into production to improve customer experiences. The role involves collaboration with data scientists, software engineers, and product teams, with a focus on recommendation systems, search ranking, or personalization at consumer scale.

What you'd actually do

  1. Define and drive the strategic vision for machine learning initiatives across multiple teams or projects.
  2. Lead the development and optimization of state-of-the-art machine learning models.
  3. Oversee the preprocessing and analysis of large datasets.
  4. Deploy and maintain ML solutions in production environments.
  5. Collaborate with cross-functional teams to integrate ML models into products and services.

Skills

Required

  • ML frameworks like TensorFlow, PyTorch, or scikit-learn
  • Cloud platforms (AWS, Azure, GCP)
  • Data processing and model deployment tools
  • Leading the design, implementation, and deployment of machine learning models

Nice to have

  • recommendation systems
  • search ranking
  • personalization at consumer scale
  • learning-to-rank
  • contextual bandits
  • real-time recommendation systems
  • social platform ML: feed ranking, social graph models, content discovery, or network growth
  • platform design skills — feature stores, model serving, experiment infrastructure
  • graph-based ML: social graph embeddings, transaction graphs, or knowledge graphs
  • data engineering instincts — BigQuery, Spark, Airflow, dbt

What the JD emphasized

  • Deep expertise with ML frameworks like TensorFlow, PyTorch, or scikit-learn
  • Proven track record of leading the design, implementation, and deployment of machine learning models
  • Production experience with learning-to-rank, contextual bandits, or real-time recommendation systems serving millions of users
  • Track record building ML that drives business metrics — you think in terms of engagement, conversion, and retention, not just model accuracy
  • Strong platform design skills — feature stores, model serving, experiment infrastructure

Other signals

  • Deploy and maintain ML solutions in production environments
  • Collaborate with cross-functional teams to integrate ML models into products and services
  • Monitor and evaluate the performance of deployed models
  • Lead the development of new methodologies and frameworks for ML applications