AI & Machine Learning in Finance Jobs—36 Live Roles

Generative AI has reset hiring across financial services, and this page tracks where that demand actually lands. FinJobsly indexes ML, applied-science, MLOps and data-leadership roles at fintechs, neobanks, exchanges, asset managers and traditional banks — including LLM-product teams that didn't exist 18 months ago. Each listing is auto-tagged with the stack (PyTorch, TensorFlow, JAX), the modelling domain (NLP, tabular, RL, time-series) and whether the role is research-leaning or production-leaning.

Applied scientists and ML engineers building risk, fraud, AML and credit-decisioning systems still make up the largest single bucket here, with strong, steady demand from companies like SoFi, Klarna, Plaid, Brex, Chime and the major card networks. On top of that, a fast-growing slice of postings target LLM and copilot product teams: retrieval-heavy systems, agentic workflows, evals, and embeddings infrastructure tuned to financial documents.

Quant and trading hiring overlaps materially with this page: market-makers, hedge funds and crypto trading firms increasingly hire for the same skill sets — Python, C++, statistical modelling, low-latency systems — under different titles. The keyword search treats those roles as first-class citizens, so a search for “quant”, “forecasting” or “alpha” will surface them alongside fintech ML roles.

Salary bands on this page skew higher than general fintech engineering: senior ML and applied-science roles in London, New York and remote-EMEA routinely clear well above standard backend levels, especially when the role touches trading, risk capital or LLM product. Filters let you narrow by seniority, remote/hybrid, contract type and the published salary range, and every apply link goes straight to the employer's ATS.