Machine Learning Engineer

Weyerhaeuser•Seattle, WA

About The Position

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.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field; equivalent practical experience will be considered.
2-4 years of experience developing or supporting machine learning systems, data platforms, or cloud-native software services. Experience in an enterprise environment is preferred.
Practical experience with elements of the model lifecycle, such as training pipelines, model registries, deployment approaches, or monitoring.
Experience with AWS or Azure and working knowledge of containerization, orchestration, or infrastructure-as-code concepts.
Familiarity with one or more tools such as MLflow, SageMaker, Kubeflow, Airflow, or comparable orchestration and experiment-tracking frameworks.
Proficiency in Python; working knowledge of SQL; familiarity with APIs and service-based architectures.
Exposure to enterprise data platforms such as Snowflake or transactional systems such as SAP is desirable.
Working understanding of reliability, scalability, security, and cost considerations for production systems.
Ability to work effectively with technical and non-technical stakeholders and translate operational requirements into practical solutions.
Demonstrated curiosity and commitment to developing expertise in evolving MLOps practices, tools, and AI platform capabilities.

Responsibilities

Develop and maintain MLOps pipelines that support model training, validation, deployment, and retraining across AI use cases, with guidance from senior engineers and architects.
Support deployment of batch and real-time inference workloads using cloud-native services and containerized architectures, with attention to performance, reliability, and cost efficiency.
Implement and maintain monitoring for model performance, data drift, prediction quality, latency, and system health. Assist with alerting, diagnostics, and issue remediation.
Build and maintain CI/CD workflows for machine learning assets, including code, features, models, and configurations, enabling safe and repeatable releases.
Collaborate with data engineering teams to support reliable data ingestion, feature generation, and versioning for consistent model behavior across environments.
Support enterprise AI governance by implementing practices for model lineage, reproducibility, auditability, and controlled promotion across environments in alignment with Responsible AI principles.
Work with data scientists, AI engineers, product managers, IT, and cybersecurity teams to translate modeling work into production-ready services.
Contribute to shared MLOps tooling, standards, documentation, and reference architectures that accelerate AI delivery across Weyerhaeuser's AI Factory.
Identify and implement opportunities to improve reliability, automation, scalability, and developer experience across the AI delivery lifecycle.