Senior Machine Learning Engineer
Salary not listed • Copenhagen • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Infrastructure Engineer
About the job
WPP is seeking a Senior ML Infrastructure Engineer to join their Open Intelligence team in Copenhagen. This hybrid role focuses on building and maintaining scalable, production-grade infrastructure for data and ML workloads, primarily on GCP. You will collaborate with data scientists and engineers to translate research prototypes into robust systems, ensuring reliability, scalability, and operational maturity. The role involves identifying and addressing technical debt, contributing to engineering best practices, and participating in knowledge sharing to drive continuous improvement. This position is crucial for supporting the global expansion of WPP's infrastructure and deepening integrations with AdTech partners, enabling advanced AI work to deliver real-world impact.
Responsibilities
- Design, build, and maintain scalable, production-grade infrastructure for data and ML workloads on GCP.
- Collaborate with data scientists and engineers to productionize research prototypes.
- Design and implement data and ML platforms with a focus on reliability, scalability, observability, and operational maturity.
- Identify and resolve technical debt, bottlenecks, and inefficiencies in infrastructure and platform components.
- Contribute to engineering best practices in CI/CD, version control, testing, and automation.
- Participate in knowledge sharing and technical discussions to foster team improvement.
Requirements
- 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 (GCP, AWS, or Azure).
- Familiarity with ML frameworks like PyTorch or TensorFlow.
- Familiarity with MLOps tools (MLflow, Kubeflow, or similar).
- Experience with CI/CD, version control, API design, and testing best practices.
- Experience building or supporting large-scale data or ML platforms, cloud platforms, or other scalable production systems.
- Strong interest in building reliable, automated, and well-engineered platforms for AI and data workloads.
- Collaborative mindset and ability to work across disciplines in a fast-paced environment.