Director, Director, ML Engineering & Agentic Systems

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

Director of ML Engineering & Agentic Systems at PayPal, focusing on leading teams to deliver LLM-based agentic systems and consumer-facing ML products at scale within the fintech domain. The role involves driving product and technical strategy, defining engineering health objectives, and building ML platform infrastructure.

What you'd actually do

  1. Directs and controls the delivery of business and technical outcomes that comprise multiple projects and operational activities within a domain through organizational and technical leadership
  2. Defines engineering health objectives that drive organization health, engineering best practices and operational targets
  3. Drives product and technical strategy with cross-functional leaders across the enterprise to deliver Domain goals
  4. Responsible for the delivery of programs, establishing annual organizational goals and ensuring performance and results that impact both their domain and potentially enterprise
  5. Develops engineering strategy that translates into operational and tactical plans; uses ground-breaking methods to think beyond existing solutions

Skills

Required

  • 10+ years in ML/AI
  • 4+ years leading engineering teams (15+ people)
  • Production experience with LLM-based agentic systems
  • Shipping consumer-facing ML at scale
  • Building ML platform infrastructure

Nice to have

  • Fintech or payments experience
  • understanding of compliance, MFA, and transaction systems

What the JD emphasized

  • Production experience with LLM-based agentic systems — multi-agent orchestration, MCP/ACP protocols, or equivalent
  • Track record shipping consumer-facing ML at scale (search, ranking, recommendation, or personalization)
  • Experience building ML platform infrastructure: feature stores, model serving, experiment frameworks
  • Fintech or payments experience is a strong plus — understanding of compliance, MFA, and transaction systems
  • Strong technical judgment — you can review architecture docs, debug model performance, and unblock ICs

Other signals

  • Directs and controls the delivery of business and technical outcomes that comprise multiple projects and operational activities within a domain through organizational and technical leadership
  • Drives product and technical strategy with cross-functional leaders across the enterprise to deliver Domain goals
  • Responsible for the delivery of programs, establishing annual organizational goals and ensuring performance and results that impact both their domain and potentially enterprise
  • Production experience with LLM-based agentic systems — multi-agent orchestration, MCP/ACP protocols, or equivalent
  • Track record shipping consumer-facing ML at scale (search, ranking, recommendation, or personalization)
  • Experience building ML platform infrastructure: feature stores, model serving, experiment frameworks