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.

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.

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 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 will develop signals, models, and trading strategies using Python or C++. You will design research tools, data pipelines, and apply advanced statistical methods within a small team of researchers and engineers at a systematic trading firm.

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.

Python and/or C++, machine learning, and backtesting frameworks. The role researches and develops systematic statistical arbitrage strategies across global equity markets at a systematic multi-strategy hedge fund.

Develop end-to-end solutions in Python (pandas/numpy) for the Commodities RAD team, building data, research & trading platforms and tools for Portfolio Managers. Also use React/Angular for web frontends and SQL for databases.

Société Générale★3.8 (3.8k)· London

You will use Python to build, maintain, and enhance quantitative models and analytical tools, and work with Bloomberg and FactSet. This role is on the Quant, Index & ETF Research team, developing systematic investment strategies and quantitative models across multiple asset classes.

You will use Python, machine learning pipelines, and AI tools to build and maintain the quantitative trading framework for research and production. Working alongside researchers and portfolio managers, you will productionise strategies, develop tooling, and support the full lifecycle of systematic trading strategies. Qube Research & Technologies is a global quantitative and systematic investment manager.

Work with Python (pandas, NumPy) and tree-based machine learning to analyze high-frequency limit orderbook data and build predictive signals for proprietary trading. DV Trading provides liquidity to worldwide markets and hedging opportunities. You’ll collaborate with senior researchers and traders to refine production-ready models.

Design, build and optimise high-performance, latency-sensitive trading systems for an elite buy-side fund, working with an object-oriented programming language. Collaborate with traders and researchers to deliver real-time decision-making technology, leveraging advanced tooling. The role focuses on minimising latency and maximising throughput in a scalable, high-throughput environment.

You will use Python, C++, distributed computing, Linux, and agentic AI to develop classical and deep learning models on high-frequency market data. The systematic macro pod deploys these models into live trading signals and enhances research infrastructure for large-scale parameter search and streamlined productionization.

Millennium Management★3.8 (21)· London

Develop and deploy classical and deep learning models on high-frequency market data for a systematic macro pod at Millennium. Work with Python, C++, distributed computing, and Linux to build research infrastructure and productionize live trading signals. Partner with the Senior Portfolio Manager and broader technology teams.

Lead a team of trading supervisors across multiple systematic trading desks. Design process and technology for trading operations using Python. Manage and mentor junior traders, coordinate project planning, and collaborate with research and engineering on production trading.

You will use Python or C++ (with pandas, numpy, numba, multiprocessing, or GPU workflows) to design and refine systematic trading strategies, build volatility prediction and regime detection models, and run large-scale simulations. The team builds advanced algorithmic trading systems across FX and crypto, combining dynamic hedging, multi-agent grid logic, and high-frequency data insights.

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.

You will use Python and modern quantitative tools to conduct original research into systematic investing, applying machine learning, statistics, and mathematical modelling alongside a research team. The project involves developing novel alpha signals, portfolio construction techniques, and robust backtesting environments.

You will work with Python, SQL, LLMs, LRMs, and multi-agent architectures to build ML/NLP-powered solutions for alpha generation and financial workflows. The team creates alpha-generating datasets and develops intelligent agents and workflow solutions for quantitative investment use cases at RavenPack.

You will use Python, C++, Linux/UNIX, PyTorch, JAX, or TensorFlow to develop and deploy advanced ML models (Deep Learning, Reinforcement Learning, sequence models) for forecasting asset volatility. Part of a specialized research team, you will perform greenfield research on unstructured datasets to discover new alpha signals for trading at a systematic investment firm.

You'll work primarily in Python with Linux systems and distributed systems, integrating various components and third-party tools through strong scripting capabilities. Citadel Securities is a global market maker providing liquidity across fixed income and equity securities to institutional clients, and you'll design and deploy software solutions supporting trading activities and business operations across the firm.

Work with Python, pandas, NumPy, SQL and time-series databases (e.g. kdb+, InfluxDB, TimescaleDB) along with cloud platforms (AWS, Azure, GCP). The role builds and optimises data infrastructure—scalable architectures, pipelines and querying—for the Global Oil desk, supporting investment decisions and trading strategies.

You will use Python and machine learning to build alpha signals and predictive models for systematic equities trading. This role leads a core part of the European alpha research team, managing researchers and driving high-impact alpha initiatives from idea to deployment.

Quantitative researchers develop and refine trading strategies using probability, statistics, numerical analysis, stochastic optimisation, and machine learning. Susquehanna is a global quantitative trading firm that applies machine learning and advanced research to vast datasets to build and deploy systematic trading strategies.