Full-time jobs in Springfield, MO

About the role

We are seeking a Machine Learning Engineer to join the Information Technology organization at our corporate office in Springfield, MO. The Machine Learning Engineer designs, builds, tests, and optimizes machine learning systems that support enterprise AI initiatives across the business. This role is responsible for developing production-ready model code, inference logic, and reusable ML components that convert approved enterprise data into reliable operational signals, recommendations, automations, or insights. This position works closely with AI leadership, Data Science, MLOps, Data Engineering, Product/Delivery, Security, Privacy, Store Operations, Merchandising, and other cross-functional partners to implement practical AI solutions. The role must balance model quality, latency, cost, privacy, maintainability, and operational usefulness. This position requires working onsite in our Springfield, MO headquarters. Occasional travel to field locations may be required. Enjoy discounts on retail merchandise, our restaurants, world-class resorts and conservation attractions!

What you'll bring

• Minimum Degree Required: Bachelor's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, Computer Engineering, Applied Mathematics, or a related technical field, or equivalent experience.
• 8+ years of experience in software engineering, machine learning engineering, applied AI engineering, or production ML systems.
• 5+ years of hands-on experience building, training, fine-tuning, or deploying machine learning models in applied business environments.
• Strong proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, OpenCV, or equivalent tools.
• Experience with one or more ML domains such as natural language processing, forecasting, classification, recommendation systems, optimization, anomaly detection, multimodal AI, or generative AI.
• Experience building production-quality APIs, services, or batch/streaming inference components.
• Experience with Git, automated testing, code review, containerization, and collaborative engineering practices.
• Occasional travel, up to 10%, may be required for field observation, technical validation, troubleshooting, or stakeholder workshops.
• Familiarity with model optimization and deployment formats or tooling such as ONNX, TensorRT, OpenVINO, quantization, batching, or similar techniques preferred.
• Familiarity with Azure Machine Learning, Azure AI services, Databricks, MLflow, or equivalent cloud ML platforms preferred.
• Familiarity with event-driven architectures, REST APIs, message queues, data lakes, and metadata/event pipelines preferred.
• Experience with distributed inference, real-time AI systems, high-throughput event processing, or enterprise integration patterns preferred.

Benefits