You’ll build cloud-native applications for a leading global financial institution, using Java with Spring Boot and React to support critical financial operations.

You will work with backend development, databases, APIs, cloud services and production machine learning systems. This is for a growing technology business. No specific project or team size is mentioned.

Citi★3.9 (21k)· London

You will work with Java, Spring, Web3J/Web3JS, Solidity, Docker, and Hardhat/Foundry to build Citi’s Integrated Digital Assets Platform (CIDAP), integrating blockchain networks and digital asset infrastructure.

Citi★3.9 (21k)· London

Backend Java developer building the Citi Integrated Digital Assets Platform (CIDAP) using Java Spring, web3 libraries (Web3J/Web3JS), Solidity, and containerized DevOps tools (Git, Jenkins, Helm).

Monzo★3.5 (108)· London

You will build and deploy deep learning, graph-based, and sequence-based ML models using Python and SQL, with exposure to Go. You will design human-in-the-loop systems and autonomous agents to route, prioritise, and automate customer-support issues for millions of customers across thousands of support staff at Monzo. You will be one of four ML engineers embedded in product squads.

Monzo★3.5 (108)· London

You will work with LLMs, autonomous agents, RAG, sequence-based architectures, Python, SQL, and Go, developing and deploying machine learning models in production. You’ll lead the design of a human-in-the-loop system that predicts, identifies, and routes customer problems to scale support for Monzo’s banking products across multiple geographies.

Citi★3.9 (21k)· London

Work with Solidity, Web3J/Web3JS/Ethers.js/Viem, Java/Spring, Node.js, and blockchains such as Ethereum, Hyperledger Besu, Canton, and Solana, using Hardhat/Foundry/Truffle for testing. Contribute to Citi's Integrated Digital Assets Platform (CIDAP) building Web3 components for tokenization, smart contracts, and on-chain settlement.

You will work with Python, AI coding agents (e.g., Claude Code, Codex), and potentially AWS, Kubernetes, and Temporal. Unitary builds Virtual Agents — AI-powered software workers that automate complex workflows end-to-end in customer tools, focusing on commercial insurance, marketplaces, and primary healthcare.

Cint★3.5 (13)· London

Use Python and SQL for statistical modeling and machine learning to optimize Cint's programmatic research marketplace (Cint Exchange). You will analyze marketplace dynamics, conduct A/B tests, and build predictive models to guide product and pricing strategy.

Hudson River Trading★4.5 (2)· London

You will use Python, C++, Pandas, NumPy, R, or MATLAB to build and maintain predictive trading models. As an algorithm developer, you research and test novel order execution and model training methods, applying

Hudson River Trading★4.5 (2)· London

You will work with Python, Pandas, NumPy, R, and MATLAB to apply statistical analysis and machine learning to vast quantities of market data. The team builds and maintains predictive trading models for the company's high-performance trading infrastructure. You will research, develop, and test order execution methods to increase trading efficiency.

Work with Java 25, Spring Boot, Postgres, and CI/CD practices building REST APIs and microservices. The team develops backend systems for fintech products including card payments, treasury management, and crypto liquidity.

You will use TensorFlow or PyTorch, Python, and CV/graphics libraries, plus 3D engines like Unreal or Unity, to develop generative models for graphics and video. As part of the Visual Computing Group, you'll design neural networks and ML for PlayStation's game rendering and live

BNY★3.5 (5.5k)· London

You'll use Java 17/21 with Spring, Maven, Groovy, JavaScript/TypeScript (Angular), Docker, GitLab CI/CD, IntelliJ, Windsurf, Splunk, and AppDynamics to build secure backend services, batches, and APIs for a global financial services company.

Senior Software Engineer will work with Java 25, Spring Boot, PostgreSQL, REST APIs, and microservices, using TDD, DDD, and CI/CD. They will build and maintain backend systems for a fintech focused on cross-border money movement, including card payments, treasury, transactions, and crypto liquidity.

Lead Software Engineers at ARQ work with Java 25, Spring Boot, Postgres, TDD, DDD, and CI/CD to build REST APIs and microservices. The team develops fintech systems powering card payments, treasury management, transaction booking, and crypto liquidity.

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments, working with Python, petabytes of data, and a large GPU cluster. They apply statistical and ML techniques, from linear models to deep learning.

Checkout.com★3.8 (15)· London

Senior Engineering Manager leading the Disputes team, which builds tools, services, and machine learning models to help merchants manage and win chargeback disputes at scale. The team owns a full stack centered on .NET / C#, React and TypeScript front ends, Python-based ML, and event-driven microservice architectures.

Backend engineers use Python, Django, Django-REST-framework, GraphQL (Graphene), Celery, Postgres, and AWS (Packer, Consul, Terraform). They build and maintain the AI-driven energy management platform that powers utilities and optimises renewable generation. The global engineering team is around 1,400 people.

Wise★3.5 (42)· London

You will build and refine financial crime prevention systems using Java (or JVM languages) with Spring, React/JavaScript/TypeScript, and CI/CD pipelines, working 80% backend and 20% frontend. You join the Account Integrity team, which protects Wise’s global platform against fraud via machine learning and real-time transaction monitoring, and you will own projects end-to-end.

Native Kotlin and Jetpack Compose development on a consumer-facing Android app for in-store grocery trips, running on a known device inside live stores. Integrates on-device edge AI (ML Kit, TensorFlow Lite) and uses AI coding tools like Cursor or Claude Code. Reports to the Head of Android.

Senior Machine Learning Engineer at ASOS, building production ML systems for personalized outfit recommendations and style discovery. You'll work with deep learning frameworks like PyTorch/TensorFlow, distributed GPU infrastructure, and MLOps practices including CI/CD, containerization, and observability. Collaborate with ML Scientists on batch and real-time models serving millions of customers.

You'll work with PyTorch, TensorFlow, XGBoost, GPUs, CI/CD and containerisation on the Search & Recommendations team, building production-grade ML systems for personalised recommendations, product ranking and AI-driven styling at ASOS, covering the full lifecycle from experimentation to deployment.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

You’ll train ML models, work on ML libraries and workflows, and use a GPU cluster with H100/H200/B200s. Projects are real-world machine learning applications for Jane Street’s global trading business, testing ideas against noisy financial data. No team size is stated.