Senior Machine Learning Engineer

GEICO offers a competitive compensation package of USD 152,838 - 300,000 per year and a hybrid work setup in Bethesda, MD (3 days in-office, 2 days remote, with the ability to report to the local office). In this Senior Machine Learning Engineer role, you will help strengthen production ML systems by reviewing model design, building deployable services and APIs, and advancing scalable MLOps infrastructure.

You will also have access to personalized development programs, mentorship, and certification assistance, with structured support to help you grow in the areas of model development and engineering practices.

Responsibilities

• Review the architecture and implementation of machine learning models that address complex business problems, partnering with Product, Business Units, and Data Science teams.
• Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines to ensure reliable, performant systems at scale.
• Write high-quality, maintainable production-grade code that delivers machine learning models as deployable services and APIs.
• Ensure production code is modular and reusable for future machine learning initiatives.
• Debug and troubleshoot model performance issues, track key metrics, and improve reliability, speed, and efficiency in production environments.
• Own the end-to-end ML model lifecycle, including monitoring, retraining, and model version management to keep models aligned with business needs over time.
• Promote best practices in software engineering, model development, and deployment.
• Review technical decision-making and support collaboration within the team.
• Pre-hire technical screen may be required.
• Multiple positions open.

Requirements

• Bachelor’s degree in Computer Science, Business Analytics, Machine Learning, or a related technical field.
• 4 years of experience as a Software Engineer (or related occupation).
• 4 years applying machine learning techniques in production, including deep learning, reinforcement learning, and NLP.
• 4 years with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• 4 years with cloud platforms (AWS, Azure, GCP), including Docker, Kubernetes, and cloud-native ML tools (e.g., AWS SageMaker, GCP AI Platform, Azure Machine Learning).
• 4 years deploying and operating ML models in production, including system architecture and fault-tolerant design for scalability, reliability, and high availability.
• 4 years writing production-grade, maintainable code using object-oriented design principles, including SQL, Python, and Spark.
• 4 years working with distributed systems, including distributed data processing and microservices architectures to scale ML models.
• 4 years in MLOps practices, including CI, CD, automated testing, and deployment pipelines for ML models, plus data or streaming tools such as Apache Kafka, Apache Flink, and Spark Streaming.
• 4 years 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