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About This Role
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.
QualificationsEducation
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field; advanced degree is a plus.
Experience
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.
About Weyerhaeuser
We sustainably manage forests and manufacture products that make the world a better place. We’re serious about safety, driven to achieve excellence, and proud of what we do. With multiple business lines in locations across North America, we offer a range of exciting career opportunities for smart, talented people who are passionate about making a difference. We know you have a choice in your career. We want you to choose us.
About Weyerhaeuser
We sustainably manage forests and manufacture products that make the world a better place. We’re serious about safety, driven to achieve excellence, and proud of what we do. With multiple business lines in locations across North America, we offer a range of exciting career opportunities for smart, talented people who are passionate about making a difference. We know you have a choice in your career. We want you to choose us.
What We Offer:
Compensation: This role is eligible for our annual merit\-increase program, and we are targeting a salary range of $106,900\-$160,400 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 15% 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 contribution equal to 5% of your eligible pay
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.
*Attention Internal Applicants:* To ensure transparency across the organization, please have a discussion with your manager prior to applying for any new opportunities. If you need any help facilitating this conversation, please reach out to your HR Representative for guidance. For more information on how to apply, including best practices for updating your profile or partnering with HR and Recruiting, please visit our internal applicant page on Roots: wy.com/applicants
Weyerhaeuser is an equal opportunity employer. Inclusion is one of our five core values and we strive to maintain a culture where all our people feel a sense of belonging, opportunity and shared purpose. We are committed to recruiting a diverse workforce and supporting an equitable and inclusive environment that inspires people of all backgrounds to join, stay and thrive with our team.
Job Information TechnologyPrimary LocationUSA\-WA\-SeattleSchedule Full\-timeJob Level Individual ContributorJob Type ExperiencedShift Day (1st)
Salary Context
This $106K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Weyerhaeuser, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($133K) sits 25% below the category median. Disclosed range: $106K to $160K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Weyerhaeuser AI Hiring
Weyerhaeuser has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $160K - $296K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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