Level

WPP

Senior Machine Learning Engineer

AI in this role

pytorchtensorflowmlflow
ml-ops

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.
 

 

Location Requirement

This is a hybrid role based in Copenhagen, Denmark. Candidates must currently reside in Denmark or be willing to relocate independently, as relocation support is not provided for this position.

 

About Open Intelligence 

We are the Activation arm of WPP Open Intelligence. Open Intelligence is a highly strategic initiative at the intersection of data science, advertising technology, and audience insights. Our team is building the data and ML systems that power the next generation of marketing and media intelligence, deeply integrated and adopted by the largest supply-side partners in the AdTech industry. With operations spanning the US, UK, and ongoing expansion into EMEA and APAC, our system continuously interacts with up to 98% of the population in our active markets. 

Based in our Copenhagen office, you will join a broader Open Intelligence team of roughly 50 people, including 14+ data scientists and a strong group of engineers working across data and ML production systems. 

 

Who We Are Looking For 

We are looking for a Senior ML Infrastructure Engineer with a strong background in infrastructure, platform engineering, data systems, or large-scale software engineering. 

You do not need to come from a pure ML infrastructure background to succeed in this role. What matters most is that you have strong engineering fundamentals and experience building robust, scalable, production-grade systems. You may have built cloud platforms, backend services, distributed data pipelines, or internal developer tooling, and you are excited to apply that experience to systems that support modern AI and ML workloads. 

You are comfortable working close to both engineers and data scientists, translating experimental or research-oriented work into reliable, maintainable production components. You care about system design, operational excellence, automation, observability, and maintainability. You value clean interfaces, strong testing practices, and infrastructure that can scale with growing demands. 

Beyond your technical skills, you are a strong communicator who can collaborate across disciplines, explain trade-offs clearly, and contribute to a high-trust, high-output team environment.

 

Why we're hiring:

We are hiring because we need experienced engineers who can help us design for scale, improve platform reliability, reduce operational friction, and build the foundations that allow advanced AI work to deliver real-world impact. To support this product evolution, our infrastructure is undergoing massive global expansion. By the end of this year, we will be fully operational across the rest of the EMEA and APAC regions. Concurrently, we are deepening our integrations to support even more of the largest supply-side partners in the AdTech industry. 

 

What you'll be doing:

  • Design, build, and maintain scalable, production-grade infrastructure that supports data and ML workloads in the cloud, primarily on GCP. 
  • Collaborate closely with data scientists and engineering peers to translate research prototypes into robust, production-ready systems. 
  • Design and implement data and ML platforms with strong reliability, scalability, observability, and operational maturity. 
  • Identify and address technical debt, bottlenecks, and inefficiencies across infrastructure and platform components. 
  • Contribute to engineering best practices across CI/CD, version control, testing, automation, and repo maintenance. 
  • Participate in knowledge sharing, technical discussions, and continuous improvement across the team. 

 

What You Will Need 

  • 4+ years of experience in infrastructure engineering, platform engineering, data engineering, or large-scale software engineering. 
  • Strong practical experience with Python and SQL. 
  • Experience designing and operating systems in cloud environments such as GCP, AWS, or Azure. 
  • Familiarity with ML frameworks such as PyTorch or TensorFlow. 
  • Familiarity with MLOps tools such as MLflow, Kubeflow, or similar ML platforms. 
  • Experience with CI/CD, version control, API design, and testing best practices. 
  • Experience with building or supporting large-scale data or ML platforms, cloud platforms or other scalable production systems. 
  • A strong interest in building reliable, automated, and well-engineered platforms that support advanced AI and data workloads. 
  • A collaborative mindset and a willingness to work across disciplines in a fast-moving engineering environment.

 

Nice to Have 

  • Familiarity with data processing and orchestration tools such as Spark, Flink, Airflow. 
  • Experience with infrastructure and deployment tooling such as Docker, ideally Kubernetes. 
  • Experience with strongly typed languages such as Java, Go, or C++. 
  • Interest in using AI coding assistants to improve engineering productivity. 

 

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-Hybrid 

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.

How we rate this

Senior Machine Learning Engineer at WPP rates 87 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsPyTorchTensorFlowMlflow

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of TensorFlow that you've run into, and how did you work around them?
  4. What's a project where you used Mlflow hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: ML Ops, PyTorch, TensorFlow, and Mlflow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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