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About This Role
### Position
JOB TITLE: Trainee, Supply Chain Project Management
REPORTS TO: Senior Project Manager, Supply Chain \& Logistics
JOB PURPOSE:
The Dior Corporate Trainee participates in a program designed to identify and develop future talent for the organization across functional areas. By working closely with their assigned Department, the Trainee supports the organization’s short and long\-term objectives through day to day administrative and long\-term project work. Trainees across departments will collaborate to develop initiatives to improve business results, and present plans to Executive Leadership Team.
### Job responsibilities
- Support the Wholesale Operations manager in day\-to\-day scope:
+ Stock management
+ Reconciliations
+ Inventory levels
+ Customer returns
- Assist in handling purchase orders and monitor pricing of products
- Prepare and compile weekly reporting for multiple functions:
+ Merchandising
+ Purchasing
- Content Creation
+ Creation of PowerPoint decks to present to
+ Supporting creative aspects of our Sustainability taskforce
+ Newsletters
- Communication and partner closely with key internal and external partners
### Profile
- At least 6 months of prior experience
- Full\-time, 12\-month program from July 2026 through July 2027
- Must be available M\-F, 9\-6pm, Friday work from home
- Must have an extremely strong sense of urgency and ability to work independently
- Solid attention to detail and accuracy
- Strong time management skills and prioritization
- Ability to multitask, show flexibility across priorities, learn quickly and retain processes.
- Ability to communicate in a clear and concise manner with internal and external groups.
- Proficient in all Microsoft Office programs (Excel, PowerPoint, Word \& Outlook)
- Adobe photoshop and InDesign experience is a plus
- Microsoft Dynamics AX experience a plus
### Additional information
The appointed candidate will be offered a salary of $20 per hour, plus potential eligibility to participate in the Company's medical benefits plan, commuter benefits, and 401k plans with employer contributions.
ADDITIONAL INFORMATION
Christian Dior was the designer of dreams. In founding his House in 1947, marked by the revolution of the New Look, he metamorphosed his reveries into wonderful creations. Christian Dior Couture, the House of Dreams, is recognized for its French heritage and vibrant culture sublimating its unique Savoir\-faire and Creativity through empowering "metiers d’art”. Our Maison is a destination for sustainable growth \& success where we shape the future of our Talents in a positive, authentic \& generous environment. We bloom \& deliver excellence with passion, determination, courage \& optimism to offer meaningful \& daring codes.
Christian Dior Couture is part of the LVMH Group, where People Make the Difference. We value, celebrate, and welcome each unique talent and strive to create an inclusive environment providing all employees a sense of purpose. Beyond your role, we recognize the importance and passion of creating communities with shared values that enrich and impact beyond our organization. As an employee, you will have an opportunity to engage in our employee\-led communities such as Sustainability, Diversity, Equity and Inclusion, and Corporate Social Responsibility.
Christian Dior Couture provides equal employment opportunities to all employees as part of the LVMH Group, which attaches great importance to ensuring that its Maisons and their partners share a set of common rules, practices, and principles with respect to ethics, social responsibility, and protection of the environment.
*CHRISTIAN DIOR*
MAISON
-------------------------------
*“Whatever you do – whether for work or pleasure – do it with passion! Live with passion,”* wrote Christian Dior.
Well before he embarked on his destiny as a couturier, Christian Dior was fascinated with art. The young Normandy native and several friends opened an art gallery when he was just 23\. In 1938 he learned the métier of pattern cutter with Robert Piguet and three years later began working as a designer for Lucien Lelong. Right from the founding of his Maison in 1946, he transformed his dreams into irresistible creations, seeking to break with the somber war years by elevating pure joy. His visionary spirit celebrated and enchanted women the world over. In only ten years, Monsieur Dior revolutionized the conventions of elegance and femininity, designing collections infused with dreams.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Christian Dior, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Christian Dior AI Hiring
Christian Dior has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $79K - $79K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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 (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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