About this role
Salary: £48,000 - 70,000 per year
Requirements: Deep understanding of transformer architectures and sequence modelling, with the ability to reason through architectural trade-offsHands-on experience designing tokenisation schemes for heterogeneous feature types such as numerical, categorical, and temporal dataProficiency in Python, PyTorch, SageMaker, and AirflowExperience owning the full model lifecycle, from training through to serving in productionComfortable working in a small, high-ownership team on open-ended technical problemsExperience with Triton kernels or GPU-level optimisationBroader ML background spanning areas beyond deep learningExperience with large-scale transactional or financial dataBackground in ML infrastructure or MLOps Responsibilities: Work on technically interesting modelling problems in fintech, training large transformer-based models on long sequences of real-world transactional eventsDesign tokenisation schemes for numerical, categorical, and temporal featuresMake deliberate decisions about vocabulary size, sequence length, and information compoundingWork across the full model lifecycle, from data preparation through to productionTranslate research decisions into systems that set the direction for how machine learning operates across our companyContribute as part of a small, high-ownership team where your work has genuine impact on the products we ship Technologies: AirflowMachine LearningPyTorchPythonMLOps More:
We are a fintech company based in London offering a full-time role with a salary range of 81,425 to 116,438 GBP. We are looking for someone to join a small, high-ownership team working on impactful machine learning systems and large-scale modelling problems. The role involves end-to-end work across research, training, and production, with opportunities to shape how machine learning operates across our products. Candidates should include a CV in English.
last updated 36 week of 2026