Use Python, Azure, Docker, Kubernetes, Apache Airflow, and GitHub Actions to build scalable ETL pipelines, financial data pipelines, and cloud-native infrastructure. This front-office role is at a systematic equity hedge fund modernising its quantitative research platform, working directly with portfolio managers and researchers. Experience with S&P Xpressfeed, Snowflake, Bloomberg, MSCI Barra, Databricks, and LLMs is advantageous.

You'll work with C# and .NET as core systems, plus Python across quant and data groups, and may use other OO languages. The role involves designing and building high-impact trading and research platforms for a fixed-income hedge fund's front-office engineering team.

JPMorgan★3.9 (20k)· London

Lead projects on the Strategic Index Technology team, using Python, Java, C++, or C# to build and maintain the cross-markets index platform alongside Quantitative Research, Trading, and Sales.

You will work with Go and/or Python, plus Kubernetes, Docker, and cloud infrastructure. You'll support a systematic cryptocurrency trading team by maintaining live production trading and research systems, and helping to deploy strategies.

Use Python, Azure, Docker, Kubernetes, Apache Airflow, and GitHub Actions to modernize a systematic equity hedge fund’s quantitative research and engineering platform. You will build scalable ETL pipelines, cloud-native infrastructure, and research-ready datasets, working directly with portfolio managers and quantitative researchers.

Durlston Partners· London

You'll use Java, Spring Boot, Kafka, and AWS (EKS, S3), plus PostgreSQL, SQL Server, and CI/CD. The team builds the core trade processing, reconciliation, and reporting platform for a leading systematic hedge fund’s multi-asset trading, collaborating closely with traders and quants.

Hudson River Trading★4.5 (2)· London

You will use Ansible, Terraform, Microsoft Graph API, PowerShell, Microsoft Intune, Autopilot, Entra ID, and Active Directory to build configuration-as-code and infrastructure-as-code solutions. You will help scale HRT’s global hybrid Windows deployment, managing user endpoints, mission-critical servers, cloud infrastructure, and secure remote computing environments. This role is on the Enterprise Engineering team.

As a Kdb+/Q Engineer at Citadel Securities, you will design and build high-performance data and analytics platforms using Kdb+/Q, Python/Java/C++, and Linux/distributed systems for real-time and historical market data that power trading and quantitative research globally. You will collaborate with traders, researchers, and engineers on scalable solutions for large-scale market data and time-series analytics.

Work with Python, Java, C++, Linux, and kernel-bypass networking. You will develop and maintain infrastructure for low-latency market data delivery and order execution across global exchanges, supporting multiple trading desks at a quantitative investment management firm.

Develop trading algorithm implementations, options pricing, volatility calculations, and simulation frameworks in Java 21+ and Python. You will collaborate with traders, researchers, and quants on high-impact projects for IMC, a global trading firm.

Jump Trading★3.8 (11)· London

You will use advanced TypeScript, Python, and modern Web frameworks to build Web-based applications interacting with Jump Trading’s real-time trading platform. The Core Development team architects and maintains the firm’s world-class trading platform.

You'll operate vulnerability scanning tools across on-premise, cloud, and hybrid environments, using scripting/automation (e.g., Python, PowerShell). You join Qube Research & Technologies, a global quantitative and systematic investment manager, working within the security function to run the vulnerability management programme across infrastructure and cloud.

You will work primarily with C# and .NET, alongside Python used by quant and data groups. You will design, build, and enhance high‑impact trading and research platforms for a leading fixed income hedge fund, covering core trading systems and front‑office tooling.

Jupiter Asset Management★4.3 (4)· London

Work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, Azure cloud tools, large language models (LLMs), and Power BI. As a Data Scientist at this investment management firm, you build predictive models, research alternative data sources for alpha signals, and deliver data-driven solutions directly with investment professionals.

You will work with Python, TypeScript, AWS (Cognito, Lambda, Fargate, API Gateway, S3, IAM), Terraform, PostgreSQL, and AI tools (Claude, GitHub Copilot, Cursor, LLM APIs). The team builds and evolves scalable microservices for BMLL Lab & Vantage, the company’s SaaS products for data science and visualisation on large financial datasets.

Castleton Commodities International★3.2 (24)· London

The engineer will work with Python, React, Angular, TypeScript, AWS, Snowflake, and CI/CD to build cloud-native tools for the front office. The team develops real-time analytics, backtesting engines, and market analysis tools to support energy commodity trading and risk management.

You will work with C++, Rust, Python, TypeScript, and protocols like FIX, WebSocket, and HTTP to build ultra-low-latency event-driven trading systems. The team develops Flow Traders' proprietary trading platform, covering venue connectivity, trading engines, and shared platforms for high-frequency trading across hundreds of global venues.

Point72★3.6 (15)· London

You'll work with large datasets using languages like Python, C++, Java, R, or MATLAB to preprocess data and build predictive models for market dynamics and systematic trading. Point72 is a quantitative investment firm applying advanced statistical learning to market prediction. This internship involves feature engineering and data validation for model estimation across a small team environment.

Point72★3.6 (15)· London

Cubist Systematic Strategies, Point72's affiliate, seeks quantitative researchers to develop statistical and predictive models for systematic trading across equities, futures, and foreign exchange using languages including Python, C++, Java, R, and MATLAB. Researchers will independently conduct rigorous research into market anomalies, managing methodology selection, data collection, backtesting, and performance monitoring. Candidates should have 3-7 years of alpha-driven quantitative research experience and advanced degrees in finance, computer science, mathematics, physics, or related quantitative disciplines.

Goldman Sachs★3.9 (2.3k)· London

You will develop quantitative trading and market making strategies across Fixed Income, Currencies, and Commodities (FICC) products using C++, Java, or Python alongside advanced statistical analysis, machine learning, and neural networks. The role involves building pricing and risk management models, collaborating with Quant Developers and trading teams, and deploying systematic alpha strategies across interest rates, FX, credit, and commodities at Goldman Sachs' London trading desk.

LSEG★3.7 (402)· London

FTSE Russell seeks a Quantitative Engineer to build scalable applications supporting index calculations, back-testing, and analytics using Python, SQL, numpy, pandas, scipy, and cloud-native solutions with RESTful APIs and CI/CD pipelines. The role combines software engineering with quantitative finance to develop tools for index monitoring, validation, and rebalancing across equity, fixed-income, currencies, and commodities. Requires 2+ years in quantitative analytics within financial services and an advanced degree in Mathematics, Computer Science, Financial Engineering, Statistics, or Physics.

Citi★3.9 (21k)· London

You'll design and build Citi's next-generation algorithmic trading and Smart Order Routing platform using low-latency Java or Rust, collaborating with trading desks and quantitative researchers. The role involves hands-on development, architectural decisions, automated testing frameworks, and CI/CD implementation while optimizing for performance and latency across global markets. Prior experience in algorithmic trading, SOR platforms, or quantitative trading in financial institutions is required, along with expertise in low-latency system design and market microstructure knowledge.

JPMorgan★3.9 (20k)· London

You'll work with Java, low-latency real-time systems, high-throughput messaging APIs, and middleware to design and develop equities algorithmic trading strategies and execution platforms. You'll collaborate with quants, traders, and technologists globally at JPMorgan's Electronic Client Services desk—a market-leading agency flow execution provider—developing simulation environments, enhancing strategies, and maintaining the trading engine across multiple technical areas.

JPMorgan★3.9 (20k)· London

You'll develop and maintain a market-leading equities algo execution platform using Java, focusing on low-latency real-time trading systems, high-throughput messaging APIs, and simulation environments. Working across JPMorgan's Electronic Client Services desk—the top EMEA agency flow provider—you'll collaborate globally with quants, traders, and technologists on trading engine infrastructure, quantitative models, and strategy optimization. This VP-level leadership role involves designing algo strategies, enhancing performance through analytics, and driving operational stability across the global platform.

You will develop production Python systems with SQL and modern databases to build data pipelines handling terabytes of exchange traffic in real-time for Maven Securities, a proprietary trading firm. The role involves designing analytics and data infrastructure supporting traders and quant researchers across discretionary, systematic, and market-making strategies in global listed derivatives. Ideal experience includes large-scale data engineering, streaming pipelines, containerization, C++, React/TypeScript, and financial markets knowledge.