DUTIES: Review the architecture and implementation of machine learning models that solve complex business problems, working closely with Product, Business Units, and Data Science teams. Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines, ensuring that 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 that code is modular and reusable for future ML projects. Debug and troubleshoot model performance issues, track key metrics, and continuously enhance model reliability, speed, and efficiency in production environments. Review the complete lifecycle of ML models, including monitoring, retraining, and managing versions of models to ensure they continue to meet business needs over time. Promote best practices in software engineering, model development, and deployment. Review technical decision-making processes and foster collaboration within the team. May require pre-hire technical screen. Hybrid Work Policy - 3 days In-office, 2 days remote - Must be able to report to local office. Multiple positions open. Salary $152,838 to $300,000 per year.
REQUIREMENTS: 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 with: Applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments; Machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn for model development; Cloud platforms (AWS, Azure, GCP), including containerization and orchestration tools (Docker, Kubernetes) and cloud-native ML tools (e.g., AWS SageMaker, GCP AI Platform, Azure Machine Learning); Deploying and operating machine learning models in production environments, including system architecture and fault-tolerant design for scalability, reliability, and high availability; Writing production-grade, maintainable code using object-oriented design principles, including SQL, Python, and Spark for data processing supporting model development; Distributed systems, including distributed data processing and microservices architectures used to scale machine learning models in production; MLOps practices, including continuous integration (CI), continuous delivery (CD), automated testing, and deployment pipelines for machine learning models, including data/streaming pipeline tools such as Apache Kafka, Apache Flink, and Spark Streaming; and Setting up, monitoring and logging systems for tracking model performance and resource utilization in production environments.
TO APPLY: Visit, https://careers.geico.com/
