Senior II Software Engineer - Machine Learning Platform

About the role

Wise is hiring a Senior II Software Engineer for its Machine Learning Platform (MLP) team, part of Data Products & Insights. The MLP builds and maintains the infrastructure that lets data scientists develop, deploy, serve and monitor machine learning models at scale, covering model serving, training pipelines, model registry, experiment tracking, feature management and model monitoring. The goal is to turn a collection of tools into a coherent, self-service platform.

Key responsibilities

• Build and maintain core ML platform services: model serving infrastructure, training pipelines and experiment tracking.
• Evolve the platform from individual services toward a coherent, user-driven product.
• Improve scalability, reliability and operability so the infrastructure supports hundreds of models in production.
• Improve observability and monitoring across the model lifecycle.
• Work with data scientists to understand their workflows and needs, treating them as customers.
• Take part in on-call/support rotation and help reduce operational toil.
• Shape the technical and product roadmap through discovery, spikes and architectural decisions.
• Share knowledge, mentor others and raise engineering standards through design and code reviews and documentation.

Required profile

• Strong engineering background in mainstream languages such as Java or Python (preferably Python), with production systems experience.
• Platform or infrastructure engineering experience, including internal developer platforms or self-service tooling.
• Comfortable working across the stack, from infrastructure and orchestration to APIs and developer tooling.
• Clear communication, collaboration with data scientists, product managers and engineers, and a growth mindset.
• Hybrid work environment; previous ML experience is not required.

Required skills

• Kubernetes for deploying, managing and troubleshooting containerised workloads.
• Cloud infrastructure (ideally AWS), including compute, storage and networking.
• Distributed systems principles and observability and monitoring (dashboards, alerts).
• Software engineering best practices: testing, code review, CI/CD, clean and maintainable code.
• Nice to have: ML platform tooling such as MLflow or Airflow, Terraform or CDK, Spark, Flink or Kafka, and streaming or batch data processing.