Interested in this AI/ML Engineer role at Ralph Lauren?
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Ref#: W180731
Department: Strategic Planning & Business Development
City: New York
State/Province: New York
Location: United States
Pay Range: $182,000 - $370,425 annually; actual pay is dependent on experience and geographic location
Company DescriptionRalph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world's most widely recognized families of consumer brands.
At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.
Position OverviewRalph Lauren is seeking a strategic, innovative, and execution-oriented Head of AI Strategy & Product Management to lead the development and scaling of the company’s enterprise AI strategy, product portfolio, and operating model through the AI Center of Excellence (CoE). This role will define how artificial intelligence capabilities are translated into scalable products, platforms, and enterprise solutions that drive measurable business value across consumer, commercial, operational, and corporate domains.
This leader will sit at the intersection of AI strategy, product management, enterprise transformation, and business innovation — responsible for shaping Ralph Lauren’s enterprise AI roadmap, governance frameworks, prioritization models, and product operating practices. The role will ensure AI initiatives align to enterprise priorities, organizational readiness, and long-term strategic differentiation while accelerating adoption and value realization globally.
This role requires a leader who combines strategic vision, product management expertise, operational rigor, commercial thinking, and AI fluency with the ability to navigate ambiguity, influence senior stakeholders, and build scalable frameworks that enable enterprise-wide transformation.
Essential Duties & Responsibilities1. Enterprise AI Strategy & Roadmap
- Define and maintain Ralph Lauren’s enterprise AI strategy, priorities, and multi-year roadmap aligned to business and technology objectives.
- Identify and evaluate opportunities where AI can create measurable value across consumer, merchandising, operations, supply chain, marketing, and corporate functions.
- Deliver ongoing market intelligence on emerging AI technologies, competitors, platforms, and industry trends to inform enterprise strategy and investment decisions.
- Establish enterprise AI success metrics, KPIs, and value realization frameworks tied to business outcomes, operational efficiency, and adoption.
- Partner with executive leadership to align AI priorities and ensure strategic consistency across the organization.
2. AI Product Management & Portfolio Leadership
- Establish and lead enterprise AI product management practices, governance models, and lifecycle management standards.
- Build and manage a prioritized portfolio of AI initiatives balancing innovation, scalability, feasibility, business value, and organizational readiness.
- Partner with Engineering, Data and Architecture teams to develop scalable AI-enabled products, platforms, intelligent agents, and enterprise solutions.
- Define frameworks for AI intake, prioritization, experimentation, launch, adoption, optimization, and lifecycle management.
- Drive alignment on AI roadmaps, funding, dependencies, and delivery sequencing across global teams.
3. AI Operating Model & Governance
- Design and operationalize enterprise AI governance frameworks, operating models, and prioritization processes.
- Define standards and guardrails for responsible AI, risk management, security, compliance, and model governance.
- Translate enterprise AI strategy into scalable operating structures including pods, governance cadences, delivery models, and organizational workflows.
- Partner with Finance, Portfolio Management, and Technology leadership on AI investment planning, funding allocation, and resource prioritization.
- Develop reusable playbooks, frameworks, and best practices that accelerate enterprise AI maturity and adoption globally.
4. AI Innovation & Transformation
- Lead strategic exploration of emerging AI technologies and evaluate their applicability to Ralph Lauren’s business and operating model.
- Identify opportunities to improve productivity, automation, decision-making, and business performance through AI-enabled capabilities.
- Support innovation programs, experimentation initiatives, and pilot programs that accelerate enterprise AI transformation.
- Drive cross-functional collaboration to scale successful AI solutions into sustainable enterprise capabilities.
- Serve as a strategic advisor to senior leadership on AI trends, opportunities, risks, and transformation priorities.
5. Leadership & Organizational Development
- Build and lead a high-performing global AI Strategy & Product Management organization.
- Attract, develop, and retain top AI, product, and strategy talent.
- Foster strong collaboration across Product, Engineering, Data Science, Commercial, Operations, and Corporate teams.
- Promote a culture of innovation, agility, accountability, experimentation, and responsible AI adoption.
Establish clear governance, communication, and decision-making practices across the AI CoE ecosystem.
Experience, Skills, and KnowledgeExperience & Education
- 10+ years of experience in AI strategy, digital product management, enterprise transformation, management consulting, or related leadership roles.
- Proven experience building and scaling enterprise AI products, platforms, operating models, or transformation initiatives.
- Demonstrated success leading complex cross-functional programs across Product, Technology, Operations, Data, and Business organizations.
- Experience operating within large global organizations and matrixed stakeholder environments preferred.
Leadership & Strategic Skills
- Strong strategic thinking, operating model design, and enterprise transformation experience.
- Demonstrated ability to influence senior leadership and drive alignment across complex organizations.
- Executive-level communication, storytelling, and presentation capabilities.
- Strong commercial acumen with the ability to connect AI investments to measurable business outcomes.
- Proven ability to balance innovation with operational rigor, governance, scalability, and execution.
Technical & Functional Expertise
- Lead the AI product strategy and operating model, translating enterprise AI priorities into a scalable roadmap of AI, agentic AI, and automation capabilities across business domains.
- Establish AI-driven product management frameworks including experimentation, rapid prototyping, data-informed decision making, AI-assisted roadmap planning, and continuous product optimization.
- Strong understanding of AI/ML ecosystems, generative AI, large language models, enterprise AI platforms, intelligent agents, and copilots.
- Experience with AI product lifecycle management, experimentation methodologies, and agile delivery models.
- Familiarity with enterprise data ecosystems, APIs, cloud platforms, analytics, and AI infrastructure.
- Understanding of responsible AI frameworks, governance principles, security, and compliance considerations.
Strong analytical and problem-solving capabilities with a data-driven decision-making mindset.
Salary Context
This $182K-$370K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ralph Lauren, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($276K) sits 30% above the category median. Disclosed range: $182K to $370K.
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.
Ralph Lauren AI Hiring
Ralph Lauren has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Nutley, NJ, US. Compensation range: $266K - $370K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% 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 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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