Sr. Staff Machine Learning Engineer

At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities. 

Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers’ expectations while making a real impact for our company through our shared purpose. 

When you join our company, we want you to feel valued, supported and proud to work here. That’s why we offer The GEICO Pledge: Great Company, Great Culture, Great Rewards and Great Careers.

Overview: We are seeking an accomplished Senior Staff ML Engineer who will serve as the technical leader for an AI/Machine learning area. In this individual contributor role, you will drive the design, development, and optimization of advanced AIML systems and platforms, while setting the technical direction for multiple AIML sub-teams. You will provide hands-on guidance, ensure architectural excellence, and lead by example to solve complex, large-scale, cross functional problems. This role requires a minimum of 10 years of relevant experience. 

Key Responsibilities: 

Technical Architecture & System Design: 

• Architect and implement scalable, high-performance machine learning platforms and systems capable of processing large data volumes and supporting real-time decision making and workflows. 

• Design end-to-end AIML pipelines - from data ingestion and feature engineering to model training, deployment, and continuous monitoring. 

• Evaluate and integrate cutting-edge AIML frameworks and libraries to maintain a state-of-the-art technology stack. 

Technical Leadership & Expert Guidance: 

• Act as the tech lead across multiple ML feature teams, setting technical direction and ensuring consistency in design principles and best practices. 

• Provide hands-on mentorship and guidance during design reviews, code assessments, and performance tuning. 

• Lead by example in tackling complex technical challenges and driving system-wide architectural improvements. 

Innovation & Research Integration: 

• Experiment with and prototype advanced machine learning algorithms and approaches to enhance system performance, model accuracy, and interpretability.  

• Stay abreast of the latest research and industry trends, translating these insights into actionable, production-level solutions. 

• Contribute to internal technical documentation and share knowledge across teams. 

Lifecycle Management & Reliability: 

• Oversee the end-to-end lifecycle of machine learning models, ensuring robust testing, deployment, and ongoing monitoring. 

• Develop and implement systems for model monitoring, alerting, and automated retraining to maintain peak performance in production. 

• Ensure adherence to industry standards, security protocols, and regulatory compliance throughout the ML lifecycle. 

Cross-Functional Collaboration: 

• Work closely with data scientists, software engineers, and product teams to seamlessly integrate ML systems into production environments. 

• Translate complex technical concepts into actionable insights for both technical and non-technical stakeholders. 

• Foster a col