Senior Machine Learning Engineer

GEICO is hiring a Senior Machine Learning Engineer to help review and build machine learning solutions that address complex business needs, while also strengthening the systems that deliver those models in production. This hybrid role is based in Bethesda, MD, with responsibilities spanning deployable services/APIs, model performance monitoring, and scalable MLOps infrastructure.

You will partner with Product, Business Units, and Data Science teams to design reliable training and deployment pipelines, maintain production-grade code, and manage the full ML lifecycle. GEICO also indicates that multiple positions are open for this role.

What you’ll do

• Review the architecture and implementation of machine learning models used to solve complex business problems with Product, Business Units, and Data Science teams.
• Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines that remain reliable and performant at scale.
• Write maintainable, production-grade code that converts machine learning models into deployable services and APIs.
• Ensure code is modular and reusable to support future ML initiatives.
• Debug model performance issues, track key metrics, and improve model reliability, speed, and efficiency in production environments.
• Manage the end-to-end ML model lifecycle, including monitoring, retraining, and version management to keep models aligned with business requirements.
• Promote software engineering, model development, and deployment best practices across the team.
• Review technical decision-making processes and support collaboration within the team.
• May require a pre-hire technical screen.

Required qualifications

• Bachelor’s degree in Computer Science, Business Analytics, Machine Learning, or a related technical field.
• Four (4) years of experience as a Software Engineer or related occupation.
• Four (4) years of experience applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments.
• Four (4) years of experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Four (4) years of experience with cloud platforms (AWS, Azure, GCP), including containerization and orchestration tools (Docker, Kubernetes) and cloud-native ML tools (AWS SageMaker, GCP AI Platform, Azure Machine Learning).
• Four (4) 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 (4) years of experience writing production-grade, maintainable code using object-oriented design principles, including SQL, Python, and Spark for data processing that supports model development.
• Four (4) years of experience with distributed systems, including distributed data processing and microservices architectures used to scale ML models in production.
• Four (4) years of experience with MLOps practices, including CI, CD, automated testing, and deployment pipelines for ML models, including data/streaming pipeline tools such as Apache Kafka, Apache Flink, and Spark Streaming.
• Four (4) years of experience setting up, monitoring, and logging systems to track model performance and resource utilization in production.

Technologies

• TensorFlow, PyTorch, Scikit-learn
• AWS, Azure, GCP
• Docker, Kubernetes
• AWS SageMaker, GCP AI Platform, Azure Machine Learning
• SQL, Python, Spark
• Apache Kafka, Apache Flink, Spark Streaming

Location and schedule

• Bethesda, MD (Hybrid)
• Hybrid work policy: 3 days in-office, 2 days remote; must be able to report to the local office.

Compensation

USD 152,838 - 300,000 per year.

Benefits

• Competitive pay, benefits, and flexibility to support your well-being and future.
• Personalized development programs, mentorship, and certification assistance.