28 set - Monza
Klarna
In 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.Train transformer-based models on long sequences of transactionsPlan and optimize vocabulary size, sequence length,
and information compoundingSupport end-to-end model lifecycle from data preparation to production servingTranslate research decisions into production systems and ML direction for the companyDeep understanding of transformer architectures and sequence modellingExperience designing tokenisation for heterogeneous features xysqume Proficiency in Python, PyTorch, SageMaker, and AirflowExperience owning the full model lifecycle from training to serving in productionAbility to work on open-ended technical problems in a small teamautonomyproblem-solvingtokenisation design for numerical, categorical, and temporal features#J-18808-Ljbffr
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