Interested in this AI/ML Engineer role at Cornerstone Building Brands?
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Company Description
Cornerstone Building Brands is a leading manufacturer of exterior building products for residential and low-rise non-residential buildings in North America. Headquartered in Cary, N.C., we serve residential and commercial customers across the new construction and Repair & Remodel (R&R) markets. Our market-leading portfolio of products spans vinyl windows, vinyl siding, stone veneer, metal roofing, metal wall systems and metal accessories. Cornerstone Building Brands’ broad, multi-channel distribution platform and expansive national footprint includes more than 18,800 team members at manufacturing, distribution and office locations throughout North America. Corporate stewardship and Environmental, Social and Governance (ESG) responsibility are embedded in our culture. We are committed to contributing positively to the communities where we live, work and play. For more information, visit us at http://cornerstonebuildingbrands.com.
Job Description
The IT Data Science Principal provides leadership for enterprise data warehouse and artificial intelligence capabilities, ensuring the organization delivers trusted insights and scalable Data and AI solutions aligned with business strategy. This role leads people, platforms, and delivery practices across analytics engineering, BI, data science/ML, and GenAI enablement.
The Data Warehouse Engineer partners closely with Data & Analytics leadership, Information Security, Risk/Compliance, and business leaders to define priorities, manage teams and vendors, and embed governance for data, models, and AI use cases. This role drives measurable outcomes through roadmap execution, operating rhythms, and continuous improvement across Data Warehouse, analytics and AI delivery.
WHAT YOU’LL DO
- Partner in developing enterprise analytics and AI roadmap, aligning use cases and investments to business strategy, value, and feasibility.
- Establish outcome-based prioritization, intake, and funding recommendations for analytics, ML, and GenAI initiatives.
- Define success metrics and benefits tracking for analytics/AI products and services (adoption, quality, cycle time, and business impact).
- Lead, coach, and partner with multi-disciplinary teams (analytics engineers, BI developers/analysts, data scientists/ML engineers), including vendor/partner resources.
- Set clear expectations for delivery quality, documentation, testing, and responsible AI practices; build development plans and career pathways.
- Build a high-performing organization focused on customer value, operational excellence, and continuous learning across analytics and AI.
- Partner with business and functional leaders to identify, shape, and deliver analytics and AI use cases; translate needs into a clear product backlog and release plan.
- Serve as a trusted advisor on KPI design, experimentation, model performance, and adoption considerations (process, change management, training).
- Communicate priorities, timelines, risk, and decisions clearly; establish operating rhythms and executive-ready status reporting.
- Define and enforce standards for data products, semantic models, KPI definitions, model documentation, and lifecycle management.
- Ensure analytics and AI solutions comply with enterprise security, privacy, data retention, and regulatory requirements; partner with InfoSec and Risk on controls.
- Establish responsible AI practices (use-case review, human-in-the-loop where needed, monitoring for drift/bias, and incident response) and drive audit-ready evidence.
- Identify and lead opportunities to modernize the analytics and AI platform (cloud data stack, self-service BI, feature/model stores, and GenAI tooling).
- Implement scalable delivery and operations (CI/CD, automated testing, observability, cost management) to improve reliability, speed, and reuse across analytics and AI products.
- Bring a strong understanding of the manufacturing domain, preferably windows manufacturing, to align data models and analytics solutions with plant, product, and order-management processes.
- Apply advanced dimensional modeling (Kimball, Inmon, Data Vault) to design star schemas, snowflake models, fact/dimension tables, and enterprise data models.
- Lead end-to-end ETL/ELT pipeline development, including ingestion, transformation, reconciliation, and loading pipelines using tools such as SSIS, ODI, and Matillion.
- Drive data governance, data quality, and Master Data Management (MDM), including trust rules, match/merge logic, lineage analysis, DQ dashboards, reconciliation processes, and MDM rule sets.
- Apply strong SQL engineering skills, writing complex SQL, PL/SQL, stored procedures, materialized views, and CTEs, with a focus on performance tuning.
- Maintain data lineage, metadata management, and documentation, including data dictionaries, mapping documents, lineage tracking, and metadata-driven data warehouse frameworks.
- Provide leadership on data warehouse projects and cross-functional collaboration, leading data engineering teams, managing stakeholders, gathering requirements, and delivering enterprise data warehouse solutions across multiple business units.
- Bring cloud data warehousing exposure (Snowflake), including hands-on experience modernizing legacy warehouses, implementing Medallion architecture, and managing cloud-native data platforms.
Qualifications
- Bachelor’s degree in Information Technology, Data Analytics, Business, or a related field.
- 8 plus years of experience across enterprise analytics, business intelligence, and/or data science/ML delivery in a medium-to-large enterprise environment.
- Proven experience leading teams and portfolios delivering analytics products and AI solutions, including vendor management, roadmap execution, and stakeholder governance.
- Hands-on experience with Snowflake as an enterprise platform, including Cortex Analyst / Snowflake Intelligence for semantic-layer-driven natural language analytics, warehouse cost/performance tuning, and role-based access governance.
- Experience designing and operating a medallion architecture (Bronze/Silver/Gold), with clear separation between raw replication, single-source-of-truth business definitions, and consumer-specific customizations.
- Experience consolidating data from multiple heterogeneous ERP and legacy source systems (e.g., JDE, EBS, SAP based systems, and similar) into a governed enterprise platform, including reconciliation across inconsistent business definitions and buying-group/customer hierarchies.
- Strong people leadership, coaching, and stakeholder management skills.
- Ability to translate business strategy into analytics and reporting solutions.
- Ability to manage multiple priorities in a fast-paced, matrixed environment.
- Experience delivering AI/ML or GenAI solutions in production environments, including MLOps practices and cross-functional delivery.
- Exposure to responsible AI/model risk management, enterprise data governance, and Agile/DevOps delivery methodologies.
- Demonstrates practical AI literacy and the ability to thoughtfully use AI‑enabled tools to enhance productivity, decision‑making, and quality of work.
Additional Information
All your information will be kept confidential according to EEO guidelines.
Why work for Cornerstone Building Brands?
Our teams are at the heart of our purpose to positively contribute to the communities where we live, work and play. Full-time* team members receive** medical, dental and vision benefits starting day 1. Other benefits include PTO, paid holidays, FSA, life insurance, LTD, STD, 401k, EAP, discount programs, tuition reimbursement, training, and professional development.
*Full-time is defined as regularly working 30+ hours per week. **Union programs may vary depending on the collective bargaining agreement.
Cornerstone Building Brands is an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, or status as a protected veteran. You can find the Equal Employment Opportunity Poster here. You can also view Your Right to Work Poster here along with This Organizations Participation in E-Verify Poster here. If you'd like to view a copy of the company's affirmative action plan for protected veterans or individuals with disabilities or policy statement, please contact Human Resources at 281-897-7788 or [email protected]. If you have a disability and you believe that you need a reasonable accommodation in order to search for a job opening or to submit an online application, please contact Human Resources at 281-897-7788 or [email protected]. This email is used exclusively to assist disabled job seekers whose disability prevents them from being able to apply online. Only emails received for this purpose will be returned. Messages left for other purposes, such as following up on an application or technical issues not related to a disability, will not receive a response.
All your information will be kept confidential according to EEO guidelines.
California Consumer Privacy Act (CCPA) of 2018
Must be at least 18 years of age to apply.
Note to External Recruiters
Cornerstone Building Brands does not accept unsolicited resumes and will not pay fees for any candidate submissions that were not expressly authorized.
Notice of Recruitment Fraud
We have been made aware of multiple scams whereby unauthorized individuals are using Cornerstone Building Brand's name and logo to solicit potential job-seekers for employment. In some cases, job-seekers are being contacted directly, both by phone and e-mail. In other instances, these unauthorized individuals are placing advertisements for fake positions with both legitimate websites and fabricated ones. These individuals are typically promising high-paying jobs with the requirement that the job-seeker send money to pay for things such as visa applications or processing fees. Please be advised that Cornerstone Building Brands will never ask potential job-seekers for any sort of advance payment or bank account information as part of the recruiting or hiring process.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cornerstone Building Brands, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cornerstone Building Brands AI Hiring
Cornerstone Building Brands has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cary, NC, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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