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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Engineer - **Company:** Weyerhaeuser - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $106,900.0 - $160,400.0 - **Contract:** Permanent contract - **Skills:** Geographic Information Systems, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Information Systems, Computer Programming, Continuous Integration, Information Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Ansible, Standard Sql, Software Engineering, Systems Integration, Management of Software Versions, Enterprise Data Management, Feature Engineering, Data Ingestion, Snowflake, Git, Containerization, AI Platforms, Kubernetes, Information Technology, Integration Frameworks, Data Management, Machine Learning Operations, Terraform, Software Version Control, Serverless Computing, Docker, Microservices - **Published:** June 6, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/27743402/Ml-Engineer-Washington-Seattle-1314 ## About the Role Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field; advanced degree is a plus., 6-8 years of experience building and supporting production machine learning systems, data platforms, or cloud-native software services in enterprise environments. ML & Model Development Hands-on experience with end-to-end machine learning lifecycle, including feature engineering, model development, training, evaluation, and operationalizing models in production envoirnments. Cloud & Infrastructure Experience with cloud platforms such as AWS or Azure, including containerization (Docker), orchestration (Kubernetes or managed equivalents), and infrastructure-as-code (Terraform\Ansible). Data & ML Tooling Familiarity with tools such as MLflow, SageMaker, Kubeflow, Statsig, Airflow, or similar orchestration and experiment-tracking frameworks. Programming Skills Strong proficiency in Python and version control (git); working knowledge of SQL; familiarity with APIs and microservices architectures. Enterprise Data Platforms Experience integrating ML workloads with enterprise data platforms such as Snowflake and transactional systems such as SAP is highly desirable. Familiarity with geospatial data sets. Operational Mindset Strong understanding of reliability, scalability, security, and cost optimization when operationalizing models in production. Collaboration & Communication Ability to work effectively with both technical and non-technical stakeholders, translating business requirements into practical solutions. Learning Orientation Demonstrated curiosity and commitment to staying current with evolving ML practices, tools, and AI platform capabilities. ## Description 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 skilled ML Engineer to design, build, and operationalize machine learning solutions that are reliable, scalable, secure, and delivering measurable business value in production. The ML Engineer will be responsible for developing, training, deploying, and operationalizing machine learning systems across Weyerhaeuser's AI portfolio, including pricing optimization, industrial AI, geospatial analytics, and generative AI solutions. This role sits at the intersection of data science, software engineering, and cloud infrastructure, enabling the transition from experimental models to trusted, production-grade AI services. You will work closely with data scientists, AI engineers, product managers, and platform teams to build scalable ML systems that support repeatability, governance, and continuous improvement across the AI lifecycle. The ideal candidate has hands-on experience with model development, feature engineering, and operationalizing models in the production environments, along with strong software engineering fundamentals. You are motivated by solving complex business problems and building intelligent systems that scale responsibly. Primary Responsibilities Develop Machine Learning Models Design, build, and optimize machine learning models, including feature engineering, model selection, training, and validation across multiple AI use cases. Model Deployment & Serving Operationalize and deploy batch and real-time inference solutions using cloud-native services and containerized architectures, ensuring performance, reliability, and cost efficiency. ML System Design & Integration Design end-to-end ML systems that integrate seamlessly with application use cases and data platforms, supporting scalable and maintainable solutions. Monitoring & Observability Implement robust monitoring for model performance, data drift, prediction accuracy, latency, and implement retraining strategies based on feedback and evolving data. Establish alerting and diagnostics to support rapid issue detection and remediation. CI/CD for AI Systems Develop and maintain CI/CD workflows for machine learning assets, including code, features, models, and configurations, enabling safe and repeatable releases into production. Data & Feature Pipelines Collaborate with data engineering teams to ensure reliable data ingestion, feature engineering, and versioning to support consistent model behavior across environments. Design, and build pipelines that enable efficient training and inference ML workflows. Governance & Responsible AI Support enterprise AI governance by enabling model lineage, reproducibility, auditability, and controlled promotion across environments in alignment with Responsible AI principles. Cross-Functional Collaboration Partner with data scientists, AI engineers, product managers, IT, and cybersecurity teams to operationalize models into production-ready solutions. Platform Enablement Contribute to shared ML tooling, standards, and reference architectures that accelerate delivery of machine learning solutions across Weyerhaeuser's AI Factory. Continuous Improvement Identify opportunities to improve reliability, automation, scalability, and developer productivity across the AI delivery lifecycle. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [The state of MLOps - machine learning in production at enterprise scale](https://www.wearedevelopers.com/videos/369-the-state-of-mlops-machine-learning-in-production-at-enterprise-scale) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers)