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About This Role
*Interface is a global flooring and sustainability leader dedicated to rethinking how spaces work for people and the planet. Our portfolio includes Interface® carpet tile and LVT, nora® rubber flooring, and FLOR® premium area rugs. Across every brand, we innovate in a way that combines design, performance, and sustainability—without compromise.*
*Trusted by architects, designers, and building professionals worldwide, we help bring bold visions to life with solutions that deliver real, measurable impact. Building on more than 30 years of sustainability progress and industry‑first innovation, we remain ‘all in’ on our goal of becoming carbon negative by 2040, without the use of offsets.*
The Director, Enterprise Artificial Intelligence (AI) is responsible for driving the execution of Interface’s enterprise AI strategy—translating vision into scalable, secure, and value\-generating solutions across commercial and corporate functions.
This role serves as the bridge between business leaders, technology teams, and strategic partners to identify, prioritize, and deliver AI use cases that improve decision\-making, accelerate growth, and increase operational efficiency—while ensuring strong governance, security, and ethical use of AI.
The role will act as Interface’s AI execution leader, owning delivery, adoption, and measurable outcomes, not experimentation in isolation.
Key Responsibilities:
AI Strategy Execution \& Delivery
- Execute Interface’s enterprise AI roadmap, aligned to company strategy, digital priorities, and value creation goals.
- Translate strategic AI priorities into clearly defined programs, use cases, and roadmaps with measurable business outcomes.
- Lead delivery of AI initiatives across domains and functions.
- Provides Project Management services for the key AI projects.
Use Case Identification \& Business Partnership
- Partner with senior business leaders to identify, vet, and prioritize high\-impact AI opportunities.
- Drive structured use case intake, value assessment, and sequencing to ensure focus on ROI\-driven outcomes.
- Serve as a trusted advisor to business teams on where and how AI can responsibly accelerate results.
AI Architecture
- Define and lead the enterprise AI architecture strategy, establishing scalable, secure, and reusable AI platforms, services, and integration patterns
- Design and govern the enterprise AI ecosystem, including large language models (LLMs), AI agents, knowledge platforms, data foundations, vector databases, orchestration frameworks, APIs, and cloud AI services
- Architect enterprise data and knowledge foundations for AI, including data pipelines, metadata, semantic layers, retrieval\-augmented generation (RAG), knowledge management, and data quality controls required to deliver trusted AI outcomes.
- Drive AI technology standards and solution architecture reviews, evaluating emerging AI capabilities, platforms, vendors, and reference architectures while ensuring interoperability, scalability, reliability, and operational excellence across the enterprise
Governance, Risk \& Responsible AI
- Lead AI governance in partnership with Security, Legal, Privacy, and Compliance teams.
- Ensure AI solutions align with Interface standards for:
- Data privacy and security
- Ethical and responsible AI use
- Model transparency and explainability
- Establish guardrails, patterns, and standards for internal and vendor\-provided AI solutions.
Technology \& Partner Management
- Leverage strategic platforms and partners (e.g., Microsoft, Salesforce, Adobe, Workday, others) to accelerate AI adoption.
- Evaluate third\-party AI tools and embedded AI capabilities with a “buy vs. build” mindset.
- Collaborate with Enterprise Architecture and Engineering teams to ensure scalability, interoperability, and long\-term viability.
- Define and track adoption, usage, and value realization metrics.
Team Leadership \& Operating Model
- Lead and mentor a small, high\-impact AI delivery team (internal and/or hybrid with partners).
- Establish a lean operating model focused on rapid iteration, business outcomes, and continuous improvement.
- Influence without authority across global, matrixed teams.
Required Qualifications:
- Bachelor’s degree in computer science, Engineering, Business, or related field; Master’s preferred.
- 8–12\+ years of experience across digital, analytics, AI, or advanced technology roles.
- Proven experience leading enterprise AI or advanced analytics initiatives from concept through production.
- Strong understanding of:
- Applied AI / machine learning concepts (not pure research)
- Data platforms, cloud ecosystems, and modern enterprise architectures
- AI risks related to privacy, security, bias, and compliance
- Demonstrated ability to partner with senior business leaders and influence outcomes.
- Experience operating in a global, matrixed organization.
Preferred Experience:
- Experience deploying AI solutions leveraging hyperscale’s (e.g., Microsoft Azure AI / Google Cloud)
- Background in manufacturing, industrial, supply chain, or commercial B2B environments.
- Exposure to embedded AI within enterprise platforms (CRM, ERP, productivity tools).
- Experience establishing AI governance frameworks or centers of enablement
4 \- Mid\-Senior Level / Management
Learn more about Interface (NASDAQ: TILE) and our brands at interface.com and FLOR.com . Join us on Facebook , Instagram , LinkedIn , and Pinterest .
We are a VEVRAA Federal Contractor. We desire priority referrals of Protected Veterans for job openings at all locations within the State of Georgia. An Equal Opportunity Employer including Veterans and Disabled.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Interface, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Interface AI Hiring
Interface has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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