14 set - Bardi
Klarna
OverviewIn this role you will tackle technically challenging modelling problems in fintech by training transformer-based models on long sequences of real-world transactional events.
You will design tokenisation schemes for numerical, categorical, and temporal features, shaping vocabulary size, sequence length, and information compounding.
You'll oversee the full model lifecycle from data prep to production, translating research decisions into systems that steer ML at Klarna.
You join a small, high-ownership team where your work directly impacts Klarna's products.
This is a mission-driven opportunity to shape scalable ML infrastructure in fintech.
ResponsabilitàTrain transformer-based models on long sequences of transactions
Design tokenisation schemes for heterogeneous features (numerical, categorical, temporal)
Plan and optimize vocabulary size, sequence length, and information compounding
Support end-to-end model lifecycle from data preparation to production serving
Translate research decisions into production systems and ML direction for the company
Collaborate in a small, high-ownership team to deliver impactful solutions
Requisiti fondamentaliDeep understanding of transformer architectures and sequence modelling
Experience designing tokenisation for heterogeneous features
Proficiency in Python, PyTorch, SageMaker, and Airflow
Experience owning the full model lifecycle from training to serving in production
Ability to work on open-ended technical problems in a small team
autonomy
problem-solving
curiosity
transformer architectures
sequence modelling
tokenisation design for numerical, categorical, and temporal features
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