Interested in this AI/ML Engineer role at WPP?
Apply Now →About This Role
WPP is the trusted growth partner for the world's leading brands.
We unite cutting\-edge media intelligence and data solutions, world\-class creativity, next\-generation production, transformative enterprise solutions and expert strategic counsel in a single company – powered by exceptional talent and our agentic marketing platform, WPP Open, to help our clients navigate change, capture opportunity and deliver transformational growth.
We work with the world's most valuable brands and have global reach across 100\+ markets, with deep local expertise.
Our people are the key to our success. We're committed to fostering a culture of creativity, belonging and continuous learning, attracting and developing the brightest talent, and providing exciting career opportunities that help our people grow.
For more information, visit WPP.com.
About Data \& Technology Solutions
WPP's Data \& Technology Solutions is the WPP's unified global data products and technology team. We work with our agencies and clients to build data data\-driven solutions and technology product to power marketing transformation.
WPP Open is our AI platform for marketing, it connects marketing professionals, data, tools and AI in a single place. WPP Open is the simplest, safest and fastest way to realize the benefits of scaled AI – delivering better\-informed creative ideas faster, at scale and at lower cost.
We're endlessly curious and our team of thinkers, builders, creators and problem solvers are over 2,000 strong, across 20 markets around the world.
Why we're hiring:
We are seeking a senior technology leader with deep expertise in cloud architectures, data platforms, and AI/agentic solutions, with a track record of building high\-performing teams and delivering complex client outcomes. You can set strategy, establish architectural standards, and partner with executive stakeholders to modernize platforms, unlock data value, and operationalize AI safely at scale.
You are:
- A strategic practice leader who can define direction, build repeatable offerings, and drive measurable impact
- A technically credible executive (cloud, data, security, and AI) who can advise clients and guide delivery teams
- A people\-first leader who attracts, develops, and retains top talent while building an inclusive, learning culture
- Comfortable working across consulting, sales, product, and engineering to take solutions from vision to delivery
- Passionate about innovation, quality, and operational discipline (standards, governance, and KPIs)
What you'll be doing:
- Practice Leadership \& Growth Develop and execute the Cloud Architecture and AI Solutions strategy for the Tech \& AI Consulting NA practice, ensuring alignment to broader business objectives. Build and sustain strong relationships with key technology vendors and industry partners, while identifying and evaluating emerging cloud technologies and trends to provide thought leadership and adoption recommendations. Maintain a comprehensive understanding of the marketing technology landscape—including major platforms, vendors, and integration strategies—and drive innovation in solutions architecture by creating reusable frameworks, patterns, and best practices that accelerate delivery and improve quality. Contribute to the development of new service offerings and go\-to\-market strategies to support growth and differentiation.
- Operational Excellence Develop and maintain architectural standards, guidelines, and best practices for cloud architecture and agentic solutions, and establish KPIs to measure and improve practice performance. Support practice management by driving profitability and efficient resource allocation, while collaborating closely with cross\-functional partners to ensure alignment, coordination, and consistent delivery across the organization.
- Client Engagement \& Delivery Serve as a trusted advisor to clients, shaping Cloud Architecture and AI Solutions strategy, target architectures, and implementation roadmaps. Lead complex consulting engagements end\-to\-end, ensuring high\-quality delivery and exceptional client satisfaction, while providing hands\-on technical leadership and guidance to project teams to successfully implement AdTech and MarTech solutions.
- Team Leadership \& Development Build, mentor, and lead a high\-performing team of Cloud Architecture and AI Solutions consultants, fostering a culture of innovation, collaboration, and continuous learning. Provide ongoing coaching and development to grow skills and career paths, while attracting, hiring, and retaining top talent to strengthen and scale the practice.
- Business Development \& Sales Support Actively drive business development through proposal leadership, compelling client presentations, and industry networking, partnering closely with Sales to identify, shape, and convert new opportunities. Build and maintain strong relationships with key decision\-makers at priority and target clients to expand pipeline and accelerate growth.
What you'll need:
- Significant experience (10\+ years) leading cloud architecture and/or technology consulting practices, including strategy, delivery governance, and talent leadership.
- Deep knowledge of modern cloud and data architectures (e.g., landing zones, network/security patterns, IAM, observability, CI/CD, data lakehouse/warehouse patterns, real\-time/streaming where relevant).
- Experience designing and operationalizing AI solutions, including applied ML/GenAI and agentic patterns, with an understanding of risk, governance, and responsible AI.
- Demonstrated ability to set architectural standards, build reusable accelerators, and improve delivery consistency and quality.
- Strong executive communication skills with the ability to translate technical architecture into business outcomes.
- Proven experience partnering with Sales and leading pre\-sales solutioning, proposals, and executive presentations.
- People leadership experience across hiring, performance management, coaching, and building inclusive teams.
- Familiarity with the marketing technology ecosystem and common integration strategies across platforms and data environments.
Who you are:
You're open*:*We are inclusive and collaborative; we encourage the free exchange of ideas; we respect and celebrate diverse views. We are open\-minded: to new ideas, new partnerships, new ways of working.
You're optimistic*:* We believe in the power of creativity, technology and talent to create brighter futures or our people, our clients and our communities. We approach all that we do with conviction: to try the new and to seek the unexpected.
You're extraordinary: we are stronger together: through collaboration we achieve the amazing. We are creative leaders and pioneers of our industry; we provide extraordinary every day.
What we'll give you:
Passionate, inspired people – We aim to create a culture in which people can do extraordinary work.
Scale and opportunity – We offer the opportunity to create, influence and complete projects at a scale that is unparalleled in the industry.
Challenging and stimulating work – Unique work and the opportunity to join a group of creative problem solvers. Are you up for the challenge?
\#LI\-Onsite
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 around four days a week. If you require accommodations or flexibility, please discuss this with the hiring team during the interview process.
WPP is an equal opportunity employer and considers applicants for all positions without discrimination or regard to particular 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.
#### Please read our Privacy Notice (https://www.wpp.com/en/careers/wpp\-privacy\-policy\-for\-recruitment) for more information on how we process the information you provide.
Salary Context
This $155K-$390K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At WPP, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($272K) sits 25% above the category median. Disclosed range: $155K to $390K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
WPP AI Hiring
WPP has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in New York, NY, US. Compensation range: $180K - $390K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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