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About This Role
Description
At Weyerhaeuser, we are the world’s premier timberland, and forest products company. Sustainability is the founding concept of our business, and our values drive every decision to ensure we continue to lead the forestry industry in sustainability practices. And we know about sustainability – we led it in the forestry industry when we planted our first seedling by hand in 1938. We recognize that our success is dependent on the success of our people. For over 125 years, our Weyerhaeuser team has been making a difference in the world – from the seedlings we plant, to the forests and trees we nurture, we ensure every acre is managed with diligence, patience and pride. That’s the Weyerhaeuser way.
About the Role
Data and AI are foundational to how Weyerhaeuser operates, improves manufacturing performance, stewards timberlands, serves customers, manages risk, and enables better decisions. We are seeking a Senior Manager, AI & Data Governance to lead the enterprise AI and Data Governance capability within the AI organization. This leader will own the strategy, operating model, team, portfolio, and execution of governance practices that enable trusted business data products and responsible AI across the enterprise.
This is a people and governance leadership role. Data Governance is an established discipline that must evolve to become more measurable, embedded in intake and delivery, and driven through business-owned data products. AI Governance operates in a fast-emerging technology, regulatory, and risk-management landscape and requires the ability to navigate ambiguity while enabling responsible innovation. The successful candidate will build stakeholder trust, establish repeatable governance mechanisms, and make governance practical and scalable. The role partners closely with the business stewards and owners, application and engineering. It also works across business leadership, IT Governance, Enterprise Architecture, Cybersecurity, Legal, Privacy, Risk, Finance and Audit, and Product. You have a high attention to detail, but are good at seeing the big picture, and aren’t afraid to think outside the box, and champion your ideas. You have experience articulating opportunity, as well as creating and successfully managing projects. You are effective at communicating timely and relevant information to business leaders and internal partners.
Responsibilities:
People and Organizational Leadership
- Lead, hire, coach, and retain a diverse team of governance analysts; set clear expectations, define career paths, and build succession and organizational capability across Data Governance, AI Governance, stewardship, and governance operations.
- Lead effectively through governance analysts, engineering, architects, business owners, and stewards, translating enterprise strategy, governance standards, and risk objectives into durable operating practices and accountable outcomes.
- Create a culture grounded in safety, inclusion, integrity, ownership, simplicity, learning, constructive challenge, and responsible innovation.
- Build consistent governance mechanisms for intake, review, decision-making, documentation, issue resolution, stakeholder engagement, adoption, knowledge transfer, and continuous improvement across employees, contractors, and partners.
Strategy, Operating Model, and Portfolio
- Define and execute a multi-year AI and Data Governance strategy and roadmap aligned to measurable business outcomes, enterprise architecture, Data & AI priorities, and risk-management objectives.
- Own and evolve the enterprise Data Governance Charter, operating model, decision rights, stewardship framework, standards, and governance forums currently supporting enterprise data governance.
- Own and evolve the AI Governance operating model as emerging technologies, regulations, enterprise risks, and industry practices change, while enabling responsible experimentation and adoption.
- Drive the phased Governance roadmap. Assess AI coverage in the enterprise, establish classification at intake, and evaluate AI and data discovery capabilities. Own the governance capability roadmap and prioritization of governance solutions, including recommendations for build-versus-buy decisions related to governance, discovery, inventory, monitoring, and compliance capabilities.
- Create a transparent service model for intake, discovery, prioritization, governance review, risk assessment, decisioning, escalation, monitoring, support, and lifecycle management across centralized and federated teams.
- Maintain governance roadmaps, capacity plans, scorecards, operating reviews, maturity measures, and benefits tracking; reduce one-off governance through standardization, reuse, and automation while aligning work to enterprise priorities. Govern internally developed, vendor-provided, and embedded AI capabilities, including machine learning, generative AI, AI agents, and emerging AI technologies across their lifecycle.
- Define and maintain Responsible AI standards covering transparency, explainability, fairness, human oversight, reliability, privacy, security, and appropriate use, aligned to enterprise risk and business objectives.
- Monitor evolving AI regulations, industry standards, and governance practices and translate them into practical, proportionate enterprise requirements that enable responsible innovation.
Platform, Architecture, and Data Products
- Partner with the AI Architect to translate the enterprise AI Governance operating model into practical policies, standards, risk tiers, lifecycle controls, review criteria, and technical guardrails, working alongside the AI Governance Council.
- Partner with the Data Architect and data teams to govern canonical data models, business definitions, critical data elements, data contracts, authoritative sources, metadata, lineage, and reusable standards that accelerate trusted data-product delivery.
- Embed Data Governance into business and technology intake so ownership, stewardship, quality, classification, architecture, lineage, and lifecycle expectations are defined early and carried through delivery and operation.
- Advance a business data-product governance model with clear Data Owners, Data Stewards, product accountability, quality expectations, certification criteria, consumer feedback, adoption measures, and value realization.
- Lead the governance platform roadmap, including Microsoft Purview and related capabilities for glossary, catalog, lineage, classification, stewardship workflows, controls, evidence, and reporting.
- Partner with the AI Architect and AI Factory teams to define the utility, integration, prioritization, and sequencing of AI inventory, discovery, lifecycle-record, evidence, monitoring, and governance-automation capabilities so the operating model and AI Factory roadmap evolve together.
- Partner with product, engineering, platform, and architecture teams to implement governance-by-design and governance-by-code patterns that automate controls, metadata capture, quality checks, policy evidence, and ongoing monitoring.
Reliability, Governance, and Business Partnership
- Build trust with business and technology stakeholders by making governance outcomes transparent, practical, measurable, and connected to data reliability, decision quality, operational performance, risk reduction, and business value.
- Establish measurable outcomes for ownership and stewardship coverage, metadata completeness, data quality, lineage, policy compliance, data-product certification, AI risk reviews, control effectiveness, adoption, and stakeholder confidence.
- Facilitate AI Governance Council operating mechanisms and partner with the Principal AI Architect, Legal, Cybersecurity, Data Governance, Privacy, Human Resources, Risk, and Audit to evaluate AI use cases, define
Responsible AI requirements, govern AI risks and exceptions, and adapt controls as technologies, regulations, and enterprise risks evolve.
- Facilitate Enterprise Data Council operating mechanisms and relevant governance functions to align Data and AI Governance with records, retention, privacy, legal, and regulatory requirements while maintaining clear accountability across governance disciplines.
- Manage budgets, platform investments, and delivery trade-offs; communicate governance health, emerging risks, decisions, investment needs, and measurable business outcomes to technical and non-technical leaders.
- Establish and oversee governance processes for identifying, documenting, escalating, remediating, and closing Data and AI Governance risks, exceptions, policy violations, and governance incidents.
- Partners with Legal, Privacy, Cybersecurity, Risk, Audit, and business stakeholders to assess emerging risks, validate control effectiveness, and continuously improve governance practices.
Qualifications
- 10+ years of progressive experience in data governance, information management, data management, AI governance, risk management, analytics, or enterprise technology, including 7+ years leading teams, senior practitioners, or enterprise programs.
- Demonstrated success establishing and operating enterprise Data Governance capabilities in a complex, multi-business environment, including ownership, stewardship, metadata, lineage, data quality, standards, and governance forums.
- Experience working in an emerging AI, technology-risk, or regulatory environment, with the ability to evaluate new risks, establish proportionate controls, and enable responsible innovation amid ambiguity.
- Strong knowledge of data-product governance and the ability to embed governance into intake, architecture, delivery, certification, adoption, measurement, and lifecycle management.
- Experience partnering with architects, engineering teams, security, legal, privacy, risk, audit, product teams, and business leaders to translate policy and standards into practical operating mechanisms.
- Experience governing enterprise data products, canonical data models, master or reference data, and federated business-domain ownership models.
- Track record of hiring, coaching, and developing governance talent; leading through senior professionals and cross-functional stakeholders; and creating an inclusive, high-accountability culture.
- Strong executive communication and stakeholder-management skills, including the ability to build trust, influence decisions across organizational boundaries, and connect governance investments to business outcomes.
- Experience managing budgets, contractors, governance technologies, and strategic partners.
- Experience with modern cloud data and AI platforms such as Snowflake, Microsoft Fabric, Purview, Azure services, AWS services, Power BI, SAP, and related engineering and AI, Data & MLOps practices.
- Proven ability to influence technical strategy across multiple teams and organizations.
Preferred, not required:
- Experience scaling an AI Governance, responsible AI program, model-risk framework, or AI lifecycle controls.
- Experience assessing enterprise governance or discovery tooling, developing build-versus-buy recommendations, and translating capability gaps into prioritized investment roadmaps and funded implementation plans.
- Experience with Microsoft Purview or comparable data catalog, metadata, lineage, quality, and stewardship platforms.
- Experience implementing governance-by-design or governance-by-code through automated controls, workflow integration, continuous monitoring, or policy evidence.
- Experience supporting manufacturing, supply chain, forestry, natural-resources, or other industrial and asset-intensive data domains.
Education
- Bachelor's degree in Computer Science, Information Systems, Data Management, Engineering, Business, Risk Management, or a related discipline, or equivalent relevant experience.
What We Offer:
Compensation: This role is eligible for our annual merit-increase program, and we are targeting a salary range of $144,790-217,185 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 25% 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.
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 Location
USA-WA-SeattleSchedule
Full-timeJob Level
ManagerJob Type
ExperiencedShift
Day (1st) Relocation Assistance Available
Salary Context
This $144K-$217K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $144K to $217K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Weyerhaeuser AI Hiring
Weyerhaeuser has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Seattle, WA, US. Compensation range: $148K - $217K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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