GEICO offers competitive pay, benefits, and flexibility designed to support your well-being and future. You will also have access to personalized development programs, mentorship, and certification assistance, while working in a hybrid environment in Bethesda, MD (3 days in-office, 2 days remote). Multiple positions are open.
What you will do
In this Senior Machine Learning Engineering role, you will review and build production-ready machine learning models that solve complex business problems. You will partner closely with Product, Business Units, and Data Science teams to ensure solutions perform reliably at scale, and you will help translate models into deployable systems.
- Review architecture and implementation of machine learning models for complex business use cases.
- Design and build scalable infrastructure for training, automated hyperparameter tuning, and deployment pipelines.
- Develop maintainable, production-grade code that delivers models as services and APIs.
- Keep code modular and reusable to support future machine learning projects.
- Debug and troubleshoot model performance, track key metrics, and improve reliability, speed, and efficiency in production.
- Manage the end-to-end ML model lifecycle, including monitoring, retraining, and model version management.
- Apply software engineering best practices across model development and deployment.
- Review technical decision-making and promote collaboration across the team.
Required skills and experience
- 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 building models with TensorFlow, PyTorch, and Scikit-learn.
- 4 years using AWS, Azure, and GCP, including Docker, Kubernetes, and cloud-native ML tools such as AWS SageMaker, GCP AI Platform, and 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 for data processing supporting model development.
- 4 years working with distributed systems, including distributed data processing and microservices architectures that scale machine learning in production.
- 4 years applying MLOps practices, including CI/CD, automated testing, and deployment pipelines for ML models, including streaming/data 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 you will work with
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.
Pre-hire technical screen: May be required.
Compensation: USD 152,838 - 300,000 per year.
