ML Engineer

Weyerhaeuser•Seattle, WA

9d•$106,900 - $160,400

About The Position

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.

Requirements

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.
Hands-on experience with end-to-end machine learning lifecycle, including feature engineering, model development, training, evaluation, and operationalizing models in production environments.
Experience with cloud platforms such as AWS or Azure, including containerization (Docker), orchestration (Kubernetes or managed equivalents), and infrastructure-as-code (Terraform\Ansible).
Familiarity with tools such as MLflow, SageMaker, Kubeflow, Statsig, Airflow, or similar orchestration and experiment-tracking frameworks.
Strong proficiency in Python and version control (git); working knowledge of SQL; familiarity with APIs and microservices architectures.
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.
Strong understanding of reliability, scalability, security, and cost optimization when operationalizing models in production.
Ability to work effectively with both technical and non-technical stakeholders, translating business requirements into practical solutions.
Demonstrated curiosity and commitment to staying current with evolving ML practices, tools, and AI platform capabilities.

Nice To Haves

Advanced degree is a plus.
Familiarity with geospatial data sets.

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.