Machine Learning Engineer-01024948

Description Machine Learning Engineer At Weyerhaeuser, we sustainably manage forests and manufacture products that make the world a better place. With a commitment to excellence and innovation, we leverage technology to enhance operational efficiency across timberlands, wood products, and corporate functions. As we continue to scale AI across the enterprise, we are seeking a Machine Learning Engineer to help operationalize machine learning solutions and support reliable, scalable, secure delivery of measurable business value in production. The Machine Learning Engineer will contribute to building, deploying, monitoring, and operating machine learning systems across Weyerhaeuser''s AI portfolio, including pricing optimization, industrial AI, geospatial analytics, and generative AI solutions. This role works at the intersection of data science, software engineering, and cloud infrastructure, helping transition experimental models into trusted, production-grade AI services. You will work closely with data scientists, AI engineers, product managers, and platform teams to apply standardized MLOps patterns that support repeatability, governance, and continuous improvement across the AI lifecycle. The ideal candidate has practical experience with ML deployment pipelines, cloud-native infrastructure, model monitoring, and enterprise data platforms, and is motivated to grow while building systems that scale responsibly. Primary Responsibilities Operationalize Machine Learning Models: Develop and maintain MLOps pipelines that support model training, validation, deployment, and retraining across AI use cases, with guidance from senior engineers and architects. Model Deployment & Serving: Support deployment of batch and real-time inference workloads using cloud-native services and containerized architectures, with attention to performance, reliability, and cost efficiency. Monitoring & Observability: Implement and maintain monitoring for model performance, data drift, prediction quality, latency, and system health. Assist with alerting, diagnostics, and issue remediation. CI/CD for AI Systems: Build and maintain CI/CD workflows for machine learning assets, including code, features, models, and configurations, enabling safe and repeatable releases. Data & Feature Pipelines: Collaborate with data engineering teams to support reliable data ingestion, feature generation, and versioning for consistent model behavior across environments. Governance & Responsible AI: Support enterprise AI governance by implementing practices for model lineage, reproducibility, auditability, and controlled promotion across environments in alignment with Responsible AI principles. Cross-Functional Collaboration: Work with data scientists, AI engineers, product managers, IT, and cybersecurity teams to translate modeling work into production-ready services. Platform Enablement: Contribute to shared MLOps tooling, standards, documentation, and reference architectures that accelerate AI delivery across Weyerhaeuser''s AI Factory. Continuous Improvement: Identify and implement opportunities to improve reliability, automation, scalability, and developer experience across the AI delivery lifecycle.