Staff Machine Learning Engineer

PayPal PayPal · Fintech · Chennai, TN +1 · Machine Learning Engineering

Staff Machine Learning Engineer for Global Fraud Prevention team at PayPal. Focus on building data infrastructure, automation, and ML tooling for risk decisioning. Responsibilities include designing and implementing ML data pipelines, ensuring data quality, developing and deploying production solutions, and mentoring junior members. The role aims to improve fraud detection and prevention through data-driven initiatives.

What you'd actually do

  1. Lead the development and optimization of advanced machine learning models.
  2. Oversee the preprocessing and analysis of large datasets.
  3. Deploy and maintain ML solutions in production environments.
  4. Collaborate with cross-functional teams to integrate ML models into products and services.
  5. Monitor and evaluate the performance of deployed models, making necessary adjustments.

Skills

Required

  • 5+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
  • Extensive experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Expertise in cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
  • Python
  • SQL

Nice to have

  • 8+ years of experience in data science, machine learning engineering, risk modeling, or related fields.
  • Experience with LLMs, prompt engineering, RAG systems, or AI agent frameworks is a strong plus.
  • Deep expertise in anomaly detection, incident forensics, or root-cause analysis.
  • Proven ability to translate complex business challenges into effective machine learning solutions.
  • Exceptional communication skills and a collaborative mindset.
  • Strong problem-solving ability; fast learner with broad technical and business knowledge.
  • Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related field; PhD preferred.

What the JD emphasized

  • advanced machine learning models
  • large datasets
  • ML solutions in production environments
  • data-driven initiatives
  • fraud detection and prevention
  • risk decisioning at scale
  • production grade solutions
  • ML-powered products
  • robust training datasets
  • high data quality
  • reliable monitoring infrastructure
  • production-grade data pipelines
  • machine learning algorithms
  • advanced statistical models
  • exploratory analysis
  • high-impact features
  • strategic business decisions
  • fairness, explainability, and model integrity
  • rigorous validation, testing, and bias detection practices
  • senior leadership
  • high-performing team of ML engineers and data scientists
  • supervised, unsupervised, reinforcement, and deep learning

Other signals

  • fraud detection
  • risk decisioning
  • ML models
  • data pipelines
  • production grade solutions