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 write and maintain critical trading infrastructure using C/C++ or Python on Linux, working closely with engineers who design trading algorithms in a fast-paced, real-time environment. The role is with Hudson River Trading, a firm that brings a scientific approach to trading financial products.

Hudson River Trading★4.5 (2)· London

Software engineering interns work with Python, C, or C++ on independent programming projects and gain hands-on exposure to HRT's algorithmic trading research and infrastructure.

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.

Develop KDB+/q applications for real-time market data and scalable infrastructure supporting quantitative strategies at a leading global hedge fund. You will build high-performance research, analytics, and trading platforms, working with Quants and Traders to solve low-latency engineering challenges.

Work with advanced Python, large complex datasets, and a proprietary technology stack including high-performance trading systems and data platforms. As a Quantitative Researcher, you will lead independent research to discover and deploy alpha-generating systematic investment strategies across global equity markets.

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.

Use Python, LLMs, agentic frameworks, MCP servers, RAG, and REST APIs to design and deploy production-grade AI systems. You will define the AI roadmap for a global investment firm, building tools that directly impact research, trading, and business decisions. The role works with Portfolio Managers, quants, and senior leadership.

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.

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.

Founding Engineers own the full stack: Python, C++/Rust (bonus), and production data pipelines. They build risk models, backtesting infrastructure, scenario tools, and an AI-driven research layer on massive financial datasets. The team owns entire systems—from math and data to APIs and interfaces—for a live fintech serving professional investors.

You will use Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, and Azure cloud tools to build predictive models and develop reproducible data science processes alongside a team of investment professionals at a UK investment management company.

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.

Develop core eTrading MI & Analytics platform components using q and kdb+, with CI/CD, TDD/BDD, and an additional programming language. The platform captures, stores, and analyses over 3TB/5 billion daily records from electronic trading venues for risk, quant, trading, and sales stakeholders.

You will work with Python and SQL, designing and developing research and live trading infrastructure for systematic, computer-driven trading strategies across equities, futures, and foreign exchange. You will build components for both live trading and simulations as part of a trading pod.

You'll use Python, databases, APIs, and microservices architecture while building and maintaining tools to support trading risk analysis and reporting, processing large volumes of market and trade data across fixed income products.

You will work with Python, C++, HPC, and distributed systems to build tools for quant research, signal generation, and large-scale data pipelines. The team develops the infrastructure powering trading signal research, letting researchers and traders test and scale strategies across global markets.

You'll use modern C++ (Python a plus) to develop an ultra-low-latency trading engine, research platform, and cloud analytics infrastructure for a systematic quant hedge fund. Working directly on a trading desk with traders and researchers, you'll own projects end-to-end and tackle hard technical challenges critical to desk success.

You will use C/C++, STL, Boost, and Linux to build low-latency algorithmic trading systems. The team develops and supports all aspects of trading strategies, including exchange price feeds, back testing, and production environments for a tech-driven quantitative hedge fund.

You will work with C++, Python, Linux, machine-learning libraries, Google Cloud, Airflow, InfluxDB, and Grafana. This role builds data engineering and prediction tools for systematic equity trading, developing software for quantitative research, maintaining data pipelines, and implementing large-scale computation and serialization frameworks.

You’ll work with modern C++, Python, and systems-level knowledge of CPU, memory, OS, and networking. As part of a trading-desk Quant Dev team collaborating with researchers and traders, you’ll build ultra-low-latency trading engines, research platforms handling terabytes of daily data, and cloud-based analytics infrastructure for a global systematic hedge fund.

The role involves developing and maintaining in-house C++ pricing libraries, multi-threaded servers, and automated trading systems, while advancing the quantitative toolbox with new algorithms. You will work on model frameworks, signal research, execution and portfolio construction tools, using Python and C++ with QuantLib experience preferred.

Work with modern C++ (C++17/20, template metaprogramming), network programming, distributed computing, and Unix/Linux. You will design and build efficient code for the production trading and research system within a newly formed Low Latency Trading group at a systematic hedge fund.

You will work with C++17+, kernel tuning, network optimisation, and low-level profiling on Linux to build latency-critical HFT systems. The team designs end-to-end architectures for a tech-driven quant and systematic hedge fund, collaborating with FPGA, hardware, and infrastructure engineers.