At a Glance
- Tasks: Tackle exciting modelling challenges in fintech and shape machine learning systems.
- Company: Join Klarna, a leader in innovative financial technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Be part of a small, dynamic team with significant ownership and influence.
- Why this job: Make a real impact on products while working with cutting-edge technology.
- Qualifications: Expertise in transformer models, Python, and full model lifecycle management.
The predicted salary is between 70000 - 90000 £ per year.
What You Will Do
You will work on some of the most technically interesting modelling problems in fintech, training large transformer-based models on long sequences of real world transactional events. You will design tokenisation schemes for numerical, categorical, and temporal features, making deliberate decisions about vocabulary size, sequence length, and information compounding. Your work spans the full model lifecycle, from data preparation through to production and you will translate research decisions into systems that set the direction for how machine learning operates across Klarna. You will be part of a small, high ownership team where what you build has genuine impact on the products Klarna ships.
Who You Are
- Deep understanding of transformer architectures and sequence modelling, with the ability to reason through architectural tradeoffs
- Hands‑on experience designing tokenisation schemes for heterogeneous feature types such as numerical, categorical, and temporal
- Proficiency in Python, PyTorch, SageMaker, and Airflow
- Experience owning the full model lifecycle, from training through to serving in production
- Comfortable working in a small, high ownership team on open ended technical problems
Awesome to Have
- Experience with Triton kernels or GPU‑level optimisation
- Broader ML background spanning areas beyond deep learning
- Experience with large-scale transactional or financial data
- Background in ML infrastructure or MLOps
