Senior Machine Learning Engineer, Fraud Risk Modeling

Why Join GEICO? 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 on relentless innovation to exceed our customers' expectations while making a real impact on local communities nationwide. Founded in 1936, GEICO is a member of the Berkshire Hathaway family of companies and one of the largest auto insurers in the United States.

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:
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Lead the Design & Implementation of ML Models:
Lead the architecture and implementation of machine learning models, working closely with Product, Business Units, and Engineering teams.

Build Scalable Infrastructure:
Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines, ensuring that systems are reliable and performant at scale.

Write Production-Grade Code for ML Services and APIs:
Write high-quality, maintainable production-grade code that turns machine learning models into deployable services and APIs. Ensure that code is modular and reusable for future ML projects.

Optimize Model Performance and Resolve Issues:
Debug and troubleshoot model performance issues, track key metrics, and continuously enhance model reliability, speed, and efficiency in production environments.

End-to-End Model Lifecycle Management:
Own the complete lifecycle of ML models, including monitoring, retraining, and managing versions of models to ensure they continue to meet business needs over time.

Leadership and Mentorship:
Guide and mentor junior machine learning engineers, promote best practices in software engineering, model development, and deployment. Lead technical decision-making processes and foster collaboration within the team.

Collaboration Across Teams:
Collaborate with cross-functional teams (e.g., data engineering, software development, and product management) to integrate machine learning models and ensure smooth deployment and operations in production systems.

Stay Up to Date with Industry Trends:
Continuously explore and integrate new machine learning techniques and system engineering tools, ensuring the team remains at the forefront of machine learning and systems architecture practices.

• B.Sc. in Computer Science, Machine Learning, Engineering, or a related technical field.
• 6+ years of hands‑on experience applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments.
• 6+ years of experience utilizing open‑source/cloud‑agnostic components such as data warehouse (e.g. snowflake), streaming platform (e.g. Kafka), relational database (e.g. PostgreSQL), No
  
  SQL (e.g. MongoDB, Cassandra), distributed processing (e.g. Spark, Ray), workflow management (e.g. Airflow, Temporal), etc.
• 6+ years of professional software development experience with at least two general‑purpose programming languages such as Java, C++, Python or C#.
• 6+ years of experience with machine learning frameworks such as Tensor Flow, PyTorch, Scikit‑learn for model development.
• At least 4 years of experience with cloud platforms (AWS, Azure, GCP) and containerization technologies such as Docker, as well as orchestration tools like Kubernetes.
• Proven experience in deploying machine learning models in a production environment, ensuring scalability, reliability, and high availability.

• Extensive experience with object‑oriented design (OOD), design patterns,…