You will work primarily with C# and .NET, alongside Python, and the team is open to other OO backgrounds like C++, Java, or Go. The role involves designing and building high-impact trading and research platforms for a fixed income hedge fund's front-office engineering team.

A Python Developer role at a London sports betting company, working with Python 3.10+, JavaScript/TypeScript, Go, RabbitMQ, Kafka, PostgreSQL, Redis, Linux, and OpenTelemetry/Prometheus/Grafana/Zabbix. The position involves building and supporting systematic trading models alongside quants and engineers.

Use Python, C++, NumPy, pandas, scikit-learn, TensorFlow, PyTorch, kdb+/q, Docker, and custom HPC clusters. They design systematic research models across global markets, apply ML to identify predictive signals, and build production research infrastructure for volatility and market structure research.

Use Python (pandas, NumPy, SciPy, matplotlib/seaborn) and SQL with cloud-based data tools and large language models. Build predictive and statistical models to identify alpha signals from alternative data for a leading investment manager with a global presence.

Uses Python, SQL, Pandas, NumPy, SciPy, Scikit-learn, PyTorch/TensorFlow, Git, Linux, and cloud computing. Researches and develops alpha signals, systematic strategies, and portfolio construction for global equity markets at a systematic trading firm.

Work with Java (including JDK migration), Kotlin, Python, Kubernetes, ArgoCD, OpenTelemetry, and modern CI/CD pipelines to develop and maintain distributed services for ingesting and processing financial market reference data. You will modernize critical production systems, including a large-scale Java/JDK upgrade programme and improvements to platform reliability and cloud-native infrastructure.

You will use Python, C++, Java, C#, or KDB+/q to develop trading systems, research platforms, and analytics tools for a global investment fund. You'll build quantitative models and data-driven solutions alongside engineers and researchers.

Designing and productionising ML pipelines with Python, Numpy, Scipy, Pytorch, Polars, Ray, Plotly, Dash, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure). The team builds advanced sports betting analytics and trading platforms, turning prototype trading models into production-ready systems while scaling architecture for low latency.

You will work with Python, SQL, R, MATLAB, C++, Java, Oracle, Postgres, Snowflake, and Git. The Global Quantitative Research Group designs, implements, and supports margin, stress testing, and risk models for clearing houses across interest rate, equity, credit, and commodity derivatives.

You will work with Python, JavaScript/TypeScript, AWS/GCP/Azure, LLM APIs (OpenAI/Anthropic), RAG, embeddings, vector databases, and SQL to build and deploy production-grade AI systems for equity research, automating screening, idea generation, and building internal APIs and UIs for an investment 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.

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.

You will design and build Python services and APIs using AWS (Lambda, S3, ECS/EKS, Step Functions, Aurora), Kafka, and PostgreSQL/SQL Server/Snowflake to support portfolio construction, trading, and analytics. This Investment Technology role turns research tools into production-grade platforms for Portfolio Managers and Quantitative Researchers across Equities, Fixed Income, and Alternatives.

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 work with C/C++, Python, CUDA, and other low-level GPU languages, plus deep learning libraries like PyTorch, JAX, or TensorFlow, to build and optimize large-scale ML systems. You will collaborate with researchers and traders to develop frameworks for financial ML, optimize training pipelines on HPC clusters, and integrate models into low-latency production systems.

Jump Trading★3.8 (11)· London

Use Python, C++, PyTorch, TensorFlow, and JAX (plus CUDA/ROCm for HPC) to apply machine learning and deep learning research. The team of traders, quantitative researchers, and engineers uses statistical analysis and data mining to develop predictive trading models for global financial markets.

Jump Trading★3.8 (11)· London

As a core development intern, you will design, code and test the firm's distributed trading system using C++ and Python on Linux, with Git for version control, and gain experience with Agile methodologies. The team builds and maintains Jump Trading's world-class low-latency trading platform and infrastructure.

Jump Trading★3.8 (11)· London

Jump Trading seeks PhD/postdoc research scientists to apply machine learning and deep learning (transformers, SSMs) using Python, C++, PyTorch, TensorFlow, or JAX. The role involves conducting and implementing open-ended research projects to develop predictive trading models for the firm’s global financial market operations.

Jump Trading★3.8 (11)· London

You'll work with C++/Python/CUDA, PyTorch/JAX/TensorFlow, and HPC clusters to build state-of-the-art ML systems for quantitative finance. You'll collaborate with researchers and quants to develop flexible frameworks, optimize training pipelines, and integrate low-latency inference into production systems.

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.

You'll develop high-performance algorithmic trading systems and real-time execution platforms in Java, working with Kafka or Solace messaging technologies and low-latency distributed systems. The Equities Tech Algorithmic Development team at Millennium, a global alternative investment firm, builds execution technology supporting Portfolio Managers across developed and emerging markets. You'll partner with technologists and trading teams to enhance VWAP, TWAP, and other execution algorithms while ensuring performance, scalability, and regulatory compliance.

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.