Senior Machine Learning Engineer - Credit modelling
Klarna, briefly At Klarna, we're building an everyday finance network, helping over 120 million consumers across 26 countries save time and money, and worry less about their finances. Working here means taking on problems most companies never get to solve, and being hands-on enough that the interesting part of the work lands with you, not someone else — you'll build with AI, not watch it happen. This is the stretch zone. Come find out what you're capable of.
About the role
Every credit decision Klarna makes runs through a model, and every one of those models runs through a pipeline your team builds and operates. As a Lead Engineer on the Credit Modeling Pipeline team, you'll be the engineer who takes consumer credit underwriting models from a data scientist's experiment to something running reliably in production.
This is an engineering position, not a data science one with some engineering on the side. Most of your time goes into writing production code, building the infrastructure the ML pipeline depends on, and deploying models with tools like AWS SageMaker — not developing new modeling approaches from scratch. You'll work alongside a team split between Stockholm and Warsaw, and help scale Klarna's in-house data science capability as the credit risk and fraud teams grow.
What you'll do
- You'll write Python to train credit underwriting models, including tree-based models, and prepare them to run in
About the role
Every credit decision Klarna makes runs through a model, and every one of those models runs through a pipeline your team builds and operates. As a Lead Engineer on the Credit Modeling Pipeline team, you'll be the engineer who takes consumer credit underwriting models from a data scientist's experiment to something running reliably in production.
This is an engineering position, not a data science one with some engineering on the side. Most of your time goes into writing production code, building the infrastructure the ML pipeline depends on, and deploying models with tools like AWS SageMaker — not developing new modeling approaches from scratch. You'll work alongside a team split between Stockholm and Warsaw, and help scale Klarna's in-house data science capability as the credit risk and fraud teams grow.
What you'll do
- You'll write Python to train credit underwriting models, including tree-based models, and prepare them to run in