Senior Machine Learning Engineer

📋 Job Overview

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.

🏢 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.

🎯 The Role

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.

✅ Key Responsibilities

• 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.

📌 Required Qualifications

• 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.

⭐ Desirable Experience

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

🎁 Benefits

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?

🛂 Visa & Eligibility

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.