Senior Machine Learning Engineer

GEICO•Bethesda, MD

7h•Hybrid

About The Position

GEICO is hiring a Senior Machine Learning Engineer to review the architecture and implementation of machine learning models that address complex business problems. The role works closely with Product, Business Units, and Data Science teams to develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines. It also turns models into reliable, performant production services and APIs using maintainable, modular code. The engineer troubleshoots model performance, monitors and improves reliability, speed, and efficiency, and oversees the model lifecycle—including monitoring, retraining, and version management. The position promotes engineering and deployment best practices and contributes to technical decision-making and team collaboration. A pre-hire technical screen may be required. The hybrid policy requires three days in the office and two days remote, and employees must be able to report to a local office. Multiple positions are open.

Requirements

Bachelor’s degree in Computer Science, Business Analytics, Machine Learning, or a related technical field.
Four years of experience as a Software Engineer or in a related occupation.
Four years of experience applying machine learning techniques, including deep learning, reinforcement learning, and NLP, in production environments.
Four years of experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn for model development.
Four years of experience with cloud platforms (AWS, Azure, or GCP), containerization and orchestration tools (Docker and Kubernetes), and cloud-native ML tools such as AWS SageMaker, GCP AI Platform, or Azure Machine Learning.
Four years of experience deploying and operating machine learning models in production, including system architecture and fault-tolerant design for scalability, reliability, and high availability.
Four years of experience writing production-grade, maintainable code using object-oriented design principles, including SQL, Python, and Spark for data processing supporting model development.
Four years of experience with distributed systems, including distributed data processing and microservices architectures used to scale machine learning models in production.
Four years of experience with MLOps practices, including continuous integration, continuous delivery, automated testing, and machine learning deployment pipelines.
Four years of experience with data and streaming pipeline tools such as Apache Kafka, Apache Flink, and Spark Streaming.
Four years of experience setting up monitoring and logging systems to track model performance and resource utilization in production environments.
Must be able to report to a local office under the hybrid work policy.

Responsibilities

Review the architecture and implementation of machine learning models that solve complex business problems.
Work closely with Product, Business Units, and Data Science teams.
Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines.
Ensure systems are reliable and performant at scale.
Write high-quality, maintainable, production-grade code that turns machine learning models into deployable services and APIs.
Ensure code is modular and reusable for future machine learning projects.
Debug and troubleshoot model performance issues, track key metrics, and improve model reliability, speed, and efficiency in production.
Review the complete lifecycle of machine learning models, including monitoring, retraining, and model version management.
Promote best practices in software engineering, model development, and deployment.
Review technical decision-making processes and foster collaboration within the team.