Senior Machine Learning Engineer

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: