Senior Machine Learning Engineer - Credit Modelling

Location: Based in Stockholm or Milan; teams typically meet in the office 2–3 days per week. The team is split between Stockholm and Warsaw.

Salary: PLN 337,470–461,928.

Company

Klarna builds an everyday finance network serving consumers across multiple countries, with products focused on saving time and managing finances.

What you will do

• Write production Python to train consumer credit underwriting models, including tree-based models.
• Build and maintain infrastructure for feature computation, model retraining, deployment, and monitoring.
• Deploy and operate machine learning models in production using AWS SageMaker and comparable tools.
• Troubleshoot machine learning pipeline issues end to end and maintain reliable production systems.
• Collaborate with data scientists and support the growth of credit risk and fraud capabilities.

Requirements

• Production experience with Python for machine learning, including model training beyond notebook prototyping.
• Experience taking machine learning models and pipelines from development into production and operating them after launch.
• Hands-on experience with tree-based models and cloud-based machine learning workloads on AWS or a comparable platform.
• Understanding of the full software development lifecycle, including version control, testing, and code review.
• Experience collaborating with data scientists to turn models into reliable production pipelines.
• Clear spoken and written English is required.

Nice to have

• Experience in credit risk, fraud, or financial services.
• Experience building data pipelines at scale in a data science or engineering organization.
• Familiarity with model monitoring, drift detection, performance dashboards, or observability tools.
• Experience setting up CI/CD for machine learning deployment.

Culture & Benefits

• Co-located collaboration with regular office attendance.
• Work with teams in Stockholm and Warsaw while supporting in-house data science capabilities.
• Non-obvious backgrounds and diverse perspectives are welcomed.
• Final compensation depends on qualifications, skills, and experience.

Hiring process

• Submit a CV in English emphasizing concrete projects, outcomes, and costs.