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About This Role
Director of eRetail Activation - LUXE Division
Location: Hudson Yards, NY
Department: CMO - LUXE Division
Reports To: AVP, eRetail
Mission Statement As the Director of eRetail Activation for the LUXE Division at L’Oréal, you will be the strategic architect behind our brands' digital presence across our most prestigious retail partners. Your mission is to elevate the consumer journey by ensuring that our luxury brand identities are flawlessly executed in the digital space. You will lead the charge in driving online conversion, managing complex launch calendars, and fostering seamless collaboration between global creative teams and local commercial stakeholders to achieve "Best-in-class" digital shelf excellence.
Key Responsibilities
- Strategic Retailer Activation & Launch Calendar Management
- 360° Strategy: Develop and oversee a comprehensive eRetail activation strategy for all LUXE portfolio brands (e.g., Lancôme, Yves Saint Laurent, Giorgio Armani).
- Calendar Ownership: Own the master activation calendar, ensuring perfect alignment between brand priorities, retailer promotional windows, and seasonal opportunities (Black Friday, Holiday, Mother’s Day).
- Strategic Roadmap: Partner with the eRetail Strategy lead to co-manage digital shelf playbooks and the Search and Discoverability roadmap across the organization.
- Internal Content Timeline & Workflow Orchestration
- Operations Hub: Act as the central hub for content operations, managing the end-to-end timeline for all digital assets.
- Cross-Functional Bridge: Align marketing, commercial, and digital teams to ensure every product launch is supported by the right assets at the right time.
- Efficiency & Optimization: Anticipate pipeline bottlenecks and implement proactive solutions. Analyze content creation processes to recommend data-based optimizations and educate stakeholders on DAM/PIM workflows.
- Best-in-Class PDP Activation & Execution
- PDP Excellence: Set the gold standard for Product Detail Pages (PDP), ensuring high-resolution imagery, compelling storytelling, optimized A+ content, and informative video.
- Search Optimization: Implement SEO/SEM best practices within PDP copy to maximize organic findability and conversion.
- GTM Execution: Oversee the timely execution of product launches and updates. Collaborate with CGO/eKAD teams for seamless asset and copy handoff to retail partners.
- Documentation: Develop and maintain best-practice documentation for all eRetail activation processes.
- Toolkit Management & Data Integrity
- Ecosystem Ownership: Own, maintain, and develop the Digital Shelf toolkit (Salsify, NIQ Digital Shelf), ensuring total data integrity.
- Market Presence: Ensure all work is maintained in PIM/DAM to achieve a fair Share of Shelf (SOS) and Share of Voice (SOV) with retailers.
- Strategic Partnership & Cross-Functional Collaboration
- Global Liaison: Serve as the primary liaison to the Chief Digital & Marketing Officer (CDMO) Brand Asset Managers and Commercial Teams (CGO)
- Feedback Loop: Influence global asset creation by providing feedback on local retailer requirements and consumer behavior trends.
- Divisional Alignment: Partner closely with Brand, DTC, Digital, and eCommerce teams to ensure unified execution of the divisional strategy.
- Team Leadership and Talent Development
- People Management: Manage, mentor, and inspire a team of direct reports, fostering a culture of excellence, agility, and continuous learning.
- Performance: Define clear KPIs and provide regular feedback to drive professional growth.
- Resource Planning: Effectively manage team capacity to handle high-volume demands across a multi-brand division.
Qualifications
- Experience: 8-10+ years in eCommerce, Digital Marketing, or Retailer Media (Beauty/Luxury preferred).
- Leadership: Proven track record of managing high-performing teams in fast-paced environments.
- Operational Excellence: Expert project management skills within complex, matrixed organizations.
- Technical Proficiency:
- PIM/DAM: Advanced experience with Salsify and Digital Asset Management.
- Analytics: Proficiency in NIQ Digital Shelf (Profitero/Clavis) and PowerBI.
- Optimization: Familiarity with Vizit for visual performance analysis.
- Retailer Portals: Experience with Sephora, Ulta, and Department Store ecosystems.
- Stakeholder Management: Exceptional communication skills with the ability to influence internal DMI partners and external stakeholders.
- Salary Range: 138,500 - 180,000 (The actual compensation will depend on a variety of job-related factors which may include geographic location, work experience, education, and skill level)
- As an integral part of our culture at L’Oréal, wellness is at our core. We not only offer a generous Benefits Package (Medical, Dental, Vision, 401K), we also offer flexible time off, work from home days, and a pension plan! Additionally, you will have access to company perks such as Makeup, Skincare, and Fragrance! Employees receive a stipend for L'Oréal products as well as VIP Access to L’Oréal’s Internal Shop for Discounted Products, and other perks such as Monthly Mobile Allowance, discounted gym memberships, and ERGs.
Additional Benefits Information As Follows:
- Competitive Benefit Package (Medical, Dental, Vision, 401K, Pension Plan)
- Flexible Time Off (Paid Company Holidays, Paid Vacation, Vacation Buy Program, Volunteer Time, Summer Fridays & More!)
- Access to Company Perks (VIP Access to L’Oréal’s Internal Shop for Discounted Products, Monthly Mobile Allowance)
- Learning & Development Opportunities (Unlimited Access to E-learnings, Lunch & Learn Sessions, Mentorship Programs, & More!)
- Employee Resource Groups (Think Tanks and Innovation Squads)
- Access to Mental Health & Wellness Programs
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Don’t meet every single requirement? At L'Oréal, we are dedicated to building a diverse, inclusive, and innovative workplace. If you’re excited about this role but your past experience doesn’t align perfectly with the qualifications listed in the job description, we encourage you to apply anyways! You may just be the right candidate for this or other roles!
We are an Equal Opportunity Employer and take pride in a diverse environment. We would love to find out more about you as a candidate and do not discriminate in recruitment, hiring, training, promotion, or other employment practices for reasons of race, color, religion, gender, sexual orientation, national origin, age, marital or veteran status, medical condition or disability, or any other legally protected status.
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Salary Context
This $138K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At L'Oréal, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. Disclosed range: $138K to $180K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
L'Oréal AI Hiring
L'Oréal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $204,100 across 1,633 tracked positions. That's 7% 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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