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Who We Are
VML is a global creative company that combines brand experience, customer experience, and commerce to create connected brands that drive growth. Guided by a Human First philosophy, VML is known for its innovative work for brands and consistently ranks among the world's leading creative companies, including the WARC Creative 100. VML's global network spans 55+ markets with principal offices in Buenos Aires, Kansas City, London, New York, São Paulo, Shanghai and Singapore.
VML is part of WPP, the trusted growth partner for the world's leading brands. Powered by exceptional talent and our agentic marketing platform WPP Open, WPP unites cutting-edge media intelligence and data solutions, creativity, production, enterprise solutions and expert strategic counsel.
For more information, please visit www.vml.com and follow along on LinkedIn #WeAreVML.
About Us
The WPP | Ford Team is an integrated operating network made up of a dynamic multi-agency ecosystem that serves Ford Motor Company and its many diverse units around the world. This operating team provides a breadth of marketing experience for Ford across Communications, Marketing, Brand and Customer Experience, A.I., Creative, Production, Media, Strategy, Analytics, Commerce & Technology and more.
Operated under a central leadership team, it draws best-in-class talent from leading WPP agencies including VML (Brand & Customer Experience), WPP Media (Media), GTB (Precision Retail Marketing), WPP Production (Brand Design & Production), Zubi & UWG (Multicultural), and Burson (Corporate Communications/Public Relations), among other key WPP specialists. WPP | Ford team members work in a highly integrated environment, with direct exposure across virtually every discipline of modern marketing.
This role is embedded within VML as part of the WPP network.
About Ford and the Opportunity
Ford is in a period of rapid transformation, modernization, and evolution. This exciting challenge is about more than just marketing vehicles. It's about lifestyle, experiences, technology and helping to build a better world, where every person is free to move and pursue their dreams. After 123 years, this iconic brand continues to evolve, focusing on services, experiences and software, in addition to vehicles.
The Role
The AI Strategist connects the people who do the work (Shopper, Media, Web, Production, and Creative teams) with the people who build the tools. You'll spend time with marketers to understand how their work actually gets done, find where AI can make it better, and turn those opportunities into tools that last, not one-off fixes. You don't need to be an engineer. You need to be curious, resourceful, good with people, and ready to think like a builder.
What You'll Do
- Embed with marketing teams to map how work gets done today, then spot where AI can remove friction or unlock something new
- Turn messy, real-world needs into clear problem statements and briefs for our AI Architect and Product Manager
- Prototype early ideas yourself using no-code and AI tools, so teams can react to something real instead of a slide
- Think beyond the immediate ask. Shape one-off requests into durable tools that can scale across teams and grow over time.
- Own the evolution of what you launch by gathering feedback, prioritizing improvements, and planning the next version
- Help build a roadmap that balances quick client wins with longer-term capabilities
- Keep builds connected so teams don't solve the same problem twice
- Shepherd ideas from first conversation to launch, and keep stakeholders informed along the way
- Log every use case so its impact can be measured
- Advocate for the business so AI gets built with the teams who use it
What We're Looking For
- 6–8 years in marketing, advertising, or agency environments (strategy, account, production, media, CX, or similar), with exposure to product, UX, process, or innovation work
- Genuine curiosity about AI, shown through personal experimentation. Tell us what you've built, tested, or broken.
- A self-starter who sees an opportunity and runs with it without waiting for permission
- A systems thinker who enjoys untangling workflows and figuring out how the pieces fit together
- A builder's mindset. You're ready to step into product thinking: treating solutions as living products with users, versions, and a roadmap rather than as finished deliverables.
- Highly collaborative and comfortable translating between creative, business, and technical people
- Strong stakeholder skills, with the ability to earn trust from both marketing teams and technical owners
- Comfortable with ambiguity and shifting priorities
Nice to Have
- Hands-on product, UX, or service design experience (discovery, user research, journey mapping, prototyping)
- Comfort with data, and familiarity with how marketing technology fits together
- Automotive or retail marketing exposure
What You Don't Need
- Professional AI experience or a technical background. We'll teach you the technical side.
We believe the best work happens when we're together, fostering creativity, collaboration, and connection. That's why we've adopted a hybrid approach, with teams in the office an average of four days a week. If you require accommodations or flexibility, please discuss this with the hiring team during the interview process.
WPP (VML) is an equal opportunity employer and considers applicants for all positions without discrimination or regard to characteristics. We are committed to fostering a culture of respect in which everyone feels they belong and has the same opportunities to progress in their careers.
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Salary Context
This $75K-$180K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At VML, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($127K) sits 40% below the category median. Disclosed range: $75K to $180K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
VML AI Hiring
VML has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Detroit, MI, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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