Machine Learning Engineer

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.

Qualifications

• Education: Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field; equivalent practical experience will be considered.
• Experience: 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.
• MLOps & ML Systems: Practical experience with elements of the model lifecycle, such as training pipelines, model registries, deployment approaches, or monitoring.
• Cloud & Infrastructure: Experience with AWS or Azure and working knowledge of containerization, orchestration, or infrastructure-as-code concepts.
• Data & ML Tooling: Familiarity with one or more tools such as MLflow, SageMaker, Kubeflow, Airflow, or comparable orchestration and experiment-tracking frameworks.
• Programming Skills: Proficiency in Python; working knowledge of SQL; familiarity with APIs and service-based architectures.
• Enterprise Data Platforms: Exposure to enterprise data platforms such as Snowflake or transactional systems such as SAP is desirable.
• Operational Mindset: Working understanding of reliability, scalability, security, and cost considerations for production systems.
• Collaboration & Communication: Ability to work effectively with technical and non-technical stakeholders and translate operational requirements into practical solutions.
• Learning Orientation: Demonstrated curiosity and commitment to developing expertise in evolving MLOps practices, tools, and AI platform capabilities.

What We Offer:

Compensation: This role is eligible for our annual merit-increase program, and we are targeting a salary range of $98,800-$148,200 based on your level of skills, qualifications and experience. You will also be eligible for our Annual Incentive Program, which offers a cash bonus targeting 10% of base pay. Potential plan funding may range from zero to two times that target.

Benefits: When you join our team, you and your dependents will be offered coverage under our comprehensive employee benefits plan, which includes medical, dental, vision, short and long-term disability, and life insurance. We offer a pre-tax Health Savings Account option which includes a company contribution. Other benefit options are also available such as voluntary Long-Term Care and Employee Assistance Programs. We also support personal volunteerism, sponsor a host of diversity networks, promote mentoring, and provide training and development opportunities to help you chart your path to a fulfilling career.

Retirement: Employees are able to enroll in our company's 401k plan, which includes a paid company match in addition to our annual contribution equal to 5% of your base salary.

Paid Time Off or Vacation: We provide eligible employees who are scheduled to work 25 hours or more per week with 3-weeks of paid vacation to use during your first year of employment. In addition, after being employed for six months, eligible employees begin to accrue vacation for future use. We also recognize eleven paid holidays per year, providing a total of 88 holiday hours and paid parental leave for all full-time employees.