Interested in this AI/ML Engineer role at Windsor?
Apply Now →About This Role
Windsor is a dynamic, customer\-focused fashion retailer driven by an entrepreneurial spirit, a passion for innovation, and a commitment to delivering exceptional experiences. Our culture is fast\-paced, collaborative, and rooted in continuous learning, where team members are empowered to challenge the status quo, move with agility, and make meaningful contributions. As we continue to grow and evolve, Windsor has embraced transformation across every aspect of the business—from enhancing the customer journey and strengthening our digital capabilities to modernizing operations and leveraging data\-driven decision\-making. We view change as an opportunity to innovate, continuously improving how we serve our customers, support our teams, and operate our business. By combining creativity, technology, and operational excellence, we are building a future\-ready organization positioned for long\-term success while staying true to our values and the unique culture that defines Windsor.
Job Summary
The Vice President, AI Transformation \& Strategic Initiatives will report to our CEO \& President and serve as a trusted advisor to our Executive Leadership Team, leading Windsor's company\-wide AI transformation journey. This highly visible role will be instrumental in shaping the future of the business by reducing costs, increasing sales, enhancing customer experience, and strengthening decision\-making across the organization. They will work with leaders across all functions—including Marketing, Creative, Merchandising, Planning, Stores, Ecommerce, Finance, Operations, Customer Service, Human Resources, and Technology—to evaluate opportunities, build business cases, align stakeholders, and help drive implementation. The ideal candidate combines visionary thinking with a hands\-on, player\-coach leadership style. They are intellectually curious, have strong business acumen, exceptional communication skills, and a passion for continuous improvement. Most importantly, this leader will embody and champion Windsor's values while helping shape the next chapter of growth through the power of AI and strategic innovation.
Essential Job Functions \& Responsibilities:
- Develop Windsor’s AI strategy in alignment with the business strategy
- Identify and prioritize AI opportunities and use cases across all areas of the business.
- Partner with executives and department leaders to understand business challenges and uncover opportunities for automation, optimization, and innovation.
- Evaluate and prioritize potential initiatives based on ROI opportunity, financial impact, operational benefits, implementation complexity, and organizational readiness.
- Facilitate executive updates, steering committees, and decision\-making processes.
- Establish success metrics and measurement frameworks. Track project outcomes and ensure expected benefits are realized.
- Lead cross\-functional teams through discovery, planning, testing, implementation, and adoption of our AI initiatives.
- Establish governance and best practices for responsible AI usage.
- Work closely with IT to make sure Windsor has the data capability and technology/systems to enable AI effectiveness
- Stay current on emerging AI technologies, tools, and best practices.
- Lead education initiatives to raise AI fluency at Windsor
Key Qualifications \& Requirements:
- 5\-10 years of professional experience in consulting, strategy, business operations, transformation, technology, or a related field.
- Strong analytical and financial modeling skills.
- Proven ability to manage complex cross\-functional initiatives.
- Outstanding communication, presentation, and stakeholder management abilities.
- Demonstrated experience influencing senior leadership.
- Demonstrated capability to develop AI GPTs. Skills, and / or Agents to support business improvement.
- Strong organizational skills and attention to detail.
- Ability to balance strategic thinking with hands\-on execution.
- Passion for learning and applying AI technologies to real business problems
Preferred:
- Management consulting experience (McKinsey, Bain, BCG, Accenture, Deloitte, EY\-Parthenon, LEK, Oliver Wyman, or similar).
- Experience leading digital transformation or AI\-related initiatives.
- Retail, ecommerce, consumer, or fashion industry experience a plus.
- Familiarity with AI platforms, workflow automation tools, analytics platforms, and enterprise software ecosystems.
What Success Looks Like
Within the first six months this leader will:
- Understand how the business operates and what drives success
- Develop firm\-wide AI strategy, governance framework, and LLM tool management.
- Establish a company\-wide framework for evaluating AI opportunities.
- Build a prioritized portfolio of high\-impact AI initiatives.
- In addition to developing a framework to measure benefits, understand the cost implications of AI usage across the business.
- To the degree practical, understand best practices for AI across the retailing, Ecom, and fashion industries and maintain a process for keeping this current.
Within the first twelve months, this leader will:
- Start to deliver measurable productivity and efficiency gains across multiple departments
- Increase organizational adoption and understanding of AI tools. Not first six months?
- Create measurable financial value through cost savings, productivity improvements, and revenue growth initiatives.
- Become a trusted advisor to executives and business leaders across the organization.
Who Will Thrive at Windsor
This role is ideal for someone who:
- Is energized by building and creating rather than maintaining the status quo.
- Can influence without formal authority.
- Thinks like an owner and acts with urgency.
- Is comfortable working in a fast\-paced environment with evolving priorities.
- Enjoys being both strategic and hands\-on.
- Embodies Windsor's culture of continuous improvement, accountability, teamwork, and value creation.
Physical/Environmental Demands and Overtime \& Availability: Work is performed in an office environment and requires the ability to operate standard office equipment and keyboards. Must have the ability to walk short distances. Sedentary work. Exerting up to 10 pounds of force occasionally and/or a small amount of force frequently or constantly to lift, carry, push, pull or otherwise move objects. Repetitive motion. Adequate movements (motions) of the wrists, hands, and/or fingers. Team members are required to have close visual acuity to perform activities such as: preparing and analyzing data and figures; transcribing; viewing a computer terminal; extensive reading.
- *Job descriptions are merely a summary of the position. Duties and responsibilities are subject to change and may include any other that management finds necessary to successfully maintain business operations.*
WINDSOR EQUAL OPPORTUNITY EMPLOYER
Salary Context
This $200K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Windsor, 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 $185,000 based on 13,200 positions with disclosed compensation. This role's midpoint ($237K) sits 28% above the category median. Disclosed range: $200K to $275K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Windsor AI Hiring
Windsor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Fe Springs, CA, US. Compensation range: $275K - $275K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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