Interested in this AI/ML Engineer role at WPP Media?
Apply Now →Skills & Technologies
About This Role
About WPP Media
WPP is the trusted growth partner for the world's leading brands. With exceptional talent, trusted data and intelligence, and world-class partnerships – all united by our pioneering agentic marketing platform, WPP Open – we help clients navigate change, capture opportunity, and deliver transformational growth.
WPP Media is WPP's AI-driven media operating unit, bringing together media, data, and partnerships to deliver creative personalisation at scale. Connected through WPP Open and powered by Open Intelligence, clients see exactly where, how, and why their media investment is working.
For more information, visit wppmedia.com.
Role Summary and Impact
This role is within WPP Media, where you will be instrumental in establishing governance, prioritization, and lifecycle management across an expanding agent ecosystem supporting Google's global account. You'll own the roadmap for agents created across enterprise AI and workspace environments, bringing structure to intake, development, approvals, testing, launch, maintenance, and retirement. You'll work with Google stakeholders, Data and Technology Services, the WPP Media product team, subject matter experts, and agency teams to ensure agents deliver meaningful value safely, accurately, and reliably.
You'll join the Integrated AI Solutions team within the Google business, a highly visible group operating at the forefront of enterprise AI adoption. This is an opportunity to shape operating models and governance practices that are still evolving, influence how agentic workflows scale across a major global client, and connect strategic priorities with detailed execution. You'll work in an environment that values curiosity, experimentation, accountability, and practical innovation.
Key Responsibilities
- Own and maintain the agent roadmap for Google's global account, prioritizing initiatives based on strategic value, user need, business impact, and available resources.
- Establish and manage the end-to-end agent lifecycle, from initial intake and development through approval, launch, testing, maintenance, optimization, and retirement.
- Monitor the agent ecosystem across relevant enterprise AI and workspace environments to understand newly created agents, their purposes, usage, dependencies, and position within broader workflows.
- Inventory, categorize, and rationalize the agent portfolio, identifying duplicate or overlapping solutions and coordinating consolidation, optimization, or retirement where appropriate.
- Manage intake and prioritization processes for new agent requests, ensuring competing needs are assessed consistently and progressed through clear decision points.
- Coordinate approval processes for agents that use new data sources, application programming interfaces, or comparable system connections, maintaining communication and follow-through across required stakeholders.
- Establish ongoing testing and monitoring practices that assess agent safety, accuracy, reliability, data traceability, and performance after launch.
- Coordinate the dedicated agent-building team's roadmap, ensuring its capacity is focused on high-value work and that development priorities remain aligned with business needs.
- Partner with the agentic design forum or council and product teams to capture user experience issues, product feedback, platform enhancements, and workflow improvement opportunities.
- Build strong working relationships across Google, Data and Technology Services, WPP Media, subject matter expert, and agency teams to drive decisions, accountability, and progress.
Skills and Experience
- Bachelor's degree or equivalent professional experience in business, technology, communications, project management, product management, or a related discipline.
- 5-8 years of relevant experience in product management, project management, program management, product operations, technology transformation, or comparable complex operational work.
- Experience managing a product, program, portfolio, roadmap, backlog, or complex cross-functional workstream from planning through delivery.
- Ability to bring structure, prioritization, and accountability to ambiguous environments where processes and operating models are still developing.
- Strong stakeholder management skills with experience influencing senior leaders, technical teams, subject matter experts, and cross-functional partners.
- Experience managing competing requests, assessing strategic priorities, and recommending which initiatives should progress.
- Ability to understand artificial intelligence, AI agents, data sources, workflows, application programming interfaces, and related emerging technology concepts.
- Knowledge of governance, responsible AI, quality assurance, model evaluation, data traceability, or operational risk management preferred.
- Experience with enterprise AI platforms, workflow automation, digital transformation, product operations, or technology ecosystems preferred.
- Familiarity with Google Workspace, Gemini, comparable AI platforms, application programming interfaces, or model context protocol connections a plus. Proprietary WPP Media platforms can be learned in the role.
Life at WPP Media & Benefits
Our passion for shaping the next era of media is powered by our commitment to Be Extraordinary, investing in our employees to inspire transformational creativity. We also Lead Optimistically, firmly believing in and Championing Growth and Development for every individual. This commitment allows WPP Media employees to leverage the extensive global WPP Media & WPP networks to pursue their passions, build vital professional connections, and learn at the cutting edge of marketing and advertising.
We Create an Open environment built on trust and respect, where everyone feels they belong and has opportunities to progress. This inclusive culture is fostered through a variety of employee resource groups and frequent in-office events showcasing team wins, sharing thought leadership, and celebrating holidays and milestone events. Our comprehensive benefits package reflects this commitment, including competitive medical, group retirement plans, vision, and dental insurance, significant paid time off, preferential partner discounts, and employee mental health awareness days.
WPP Media is an equal opportunity employer and considers applicants for all positions without discrimination or regard to characteristics. We believe the best work happens when we're together, fostering creativity, collaboration, and connection in this open and supportive environment. 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. Please note that while our philosophy is the same across WPP, benefits may vary by office/country.
WPP Media is committed to providing equal employment opportunities to all qualified applicants and employees. Employment decisions are made without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable federal, state, or local law.
Please read our Privacy Notice (https://www.wppmedia.com/pages/privacy-policy) for more information on how we process the information you provide.
While we appreciate all applications received, only those candidates selected for an interview will be contacted.
Please read our Privacy Notice for more information on how we process the information you provide.
Salary Context
This $100K-$230K range is below the median 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 WPP Media, 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 $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 ($165K) sits 22% below the category median. Disclosed range: $100K to $230K.
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.
WPP Media AI Hiring
WPP Media has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $230K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.