Director, Machine Learning Engineering

GEICO

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Palo Alto, CANew York, NYDallas, TXBethesda, MDSeattle, WA
Salary
$150,000–$300,000 / yr
Posted
98 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $233k
This role $225k
$132k most similar roles pay here $318k

This role pays less than 53% of similar roles. Most pay $202,412–$262,600 — the shaded band above. At the midpoint, this role pays about $225k versus about $233k for comparable roles.

Based on 240 similar postings.

Employer

About GEICO

GEICO (Government Employees Insurance Company) is one of the largest auto insurers in the United States, offering affordable auto, home, renters, and other personal insurance products. Industry: Insurance

GEICO currently has 68 open roles on FindRole.

Listed pay typically runs $112,500–$230,000 across 68 roles with salary data.

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View all roles at GEICO

At a glance

TL;DR · Director, Machine Learning Engineering

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As the Director, Machine Learning Engineering, you will lead the Runtime Intelligence & Personalization function to build scalable systems that power context-aware experiences across various products and platforms. You will own the vision and strategy for intelligent runtime systems, specifically focusing on building core capabilities such as context orchestration, state management, memory systems, and retrieval-augmented generation (RAG) architectures. Your daily work involves overseeing end-to-end delivery of high-scale, low-latency systems while ensuring they are secure and observable in production environments. You will manage cross-functional teams across engineering, data science, and product functions to implement personalization frameworks. The role requires deep expertise in RAG, real-time inference, LLMs, generative AI, and agent-based systems. You will solve complex technical challenges by integrating knowledge into retrieval systems and optimizing performance, cost, and accuracy for personalized user interactions.

Skills

AI ML RAG LLMs Generative AI Distributed Systems Platform Engineering Context Orchestration Memory Systems Retrieval Systems Real-time Inference Observability System Optimization Agent-based Systems

What you'll do

  • Define and execute the roadmap for runtime intelligence capabilities including context building, memory systems, and RAG architectures.
  • Translate business needs into scalable, AI-powered personalization strategies across products and platforms.
  • Lead the development of core capabilities like context orchestration, state management, and retrieval systems.
  • Ensure high-scale, low-latency systems are secure, observable, and reliable in production environments.
  • Establish observability standards and KPIs to evaluate system performance, cost, and accuracy.
  • Oversee end-to-end delivery of runtime intelligence platforms while managing cross-functional execution across engineering and data science teams.
  • Build, lead, and scale high-performing teams across engineering, machine learning, and platform functions.
  • Communicate complex technical concepts clearly to executive and non-technical stakeholders.

What we're looking for

  • 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), context and memory architectures, and real-time inference systems.
  • Strong business acumen to translate strategy into execution and lead complex cross-functional initiatives.
  • Preferred experience with LLMs, generative AI, and agent-based systems.
  • Experience with observability, experimentation frameworks, or system optimization in high-scale environments.
  • Ability to build, lead, and scale high-performing teams across engineering, ML, and platform functions.

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