Director, Machine Learning Engineering

GEICO GEICO · Insurance · Palo Alto, CA +4

Director of Machine Learning Engineering leading Runtime Intelligence & Personalization, focusing on building scalable systems for context-aware, personalized experiences using AI/ML, platform engineering, and product strategy. Responsibilities include defining roadmaps for RAG-based architectures, context orchestration, memory systems, personalization frameworks, and ensuring secure, observable, and reliable production systems.

What you'd actually do

  1. Define and execute the roadmap for runtime intelligence capabilities, including context building, memory systems, and RAG-based architectures
  2. Lead development of core runtime capabilities: Context orchestration and state management, Memory systems (short-term and long-term), Retrieval systems (RAG, knowledge integration), Personalization frameworks
  3. Establish observability standards across runtime intelligence systems (latency, quality, accuracy, cost)
  4. Oversee end-to-end delivery of runtime intelligence platforms, ensuring scalability and reliability
  5. Build, lead, and scale high-performing teams across engineering, ML, and platform functions

Skills

Required

  • 10–15+ years of experience in engineering, platform, or AI/ML roles, with significant leadership experience
  • Proven track record building and scaling distributed systems, AI platforms, or personalization systems
  • Deep expertise in Retrieval-Augmented Generation (RAG)
  • Deep expertise in Context and memory architectures
  • Deep expertise in Real-time inference systems
  • Strong business acumen with the ability to translate strategy into execution
  • Demonstrated success leading large, complex, cross-functional initiatives

Nice to have

  • Experience with LLMs, generative AI, and agent-based systems
  • Background in observability, experimentation frameworks, or system optimization
  • Experience operating in high-scale, customer-facing environments

What the JD emphasized

  • Deep expertise in areas such as: Retrieval-Augmented Generation (RAG), Context and memory architectures, Real-time inference systems
  • Proven track record building and scaling distributed systems, AI platforms, or personalization systems

Other signals

  • runtime intelligence
  • personalization
  • RAG
  • memory systems
  • real-time inference