Director, Machine Learning Engineering

GEICO•Bethesda, MD

2d•$150,000 - $300,000

About The Position

We are seeking a strategic and execution-oriented Director to lead our Runtime Intelligence & Personalization function. This leader will own the vision, strategy, and delivery of intelligent runtime systems that power context-aware, personalized experiences across products and platforms. This role sits at the intersection of AI/ML, platform engineering, and product strategy, responsible for building scalable systems that leverage context, memory, and retrieval to deliver differentiated, real-time intelligence.

Requirements

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 areas such as: Retrieval-Augmented Generation (RAG), Context and memory architectures, 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 Haves

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

Responsibilities

Define and execute the roadmap for runtime intelligence capabilities, including context building, memory systems, and RAG-based architectures
Translate business and customer needs into scalable, AI-powered personalization strategies
Partner with executive stakeholders to align runtime intelligence investments with enterprise priorities
Act as a thought leader in shaping next-generation personalization and AI-driven experiences
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
Drive design and implementation of high-scale, low-latency systems
Ensure systems are secure, observable, and reliable in production environments
Establish observability standards across runtime intelligence systems (latency, quality, accuracy, cost)
Define KPIs and metrics to evaluate system performance and business impact
Lead continuous optimization of runtime performance, cost, and response quality
Implement feedback loops for model and system improvement
Oversee end-to-end delivery of runtime intelligence platforms, ensuring scalability and reliability
Drive cross-functional execution across engineering, data science, and product teams
Manage prioritization, trade-offs, and execution in a complex, fast-paced environment
Build, lead, and scale high-performing teams across engineering, ML, and platform functions
Develop talent strategy, including hiring, coaching, and organizational design
Foster a culture of innovation, accountability, and continuous improvement
Lead through senior managers and technical leaders to deliver outcomes at scale
Partner with Product, Design, Data, and Infrastructure teams to deliver integrated solutions
Influence senior stakeholders and drive alignment across competing priorities
Communicate complex technical concepts clearly to executive and non-technical audiences