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.

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.

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.

You will work with C++17+ in a low-latency HFT environment, performing kernel-level tuning, network optimization, and systems programming. The team builds and operates ultra-low-latency trading systems for a global quant hedge fund, collaborating with FPGA and hardware engineers on end-to-end architectures where every nanosecond counts.

You will build and optimise low-latency trading system components in modern C++ for a fully automated electronic trading platform. You will design real-time infrastructure for market data ingestion, order execution, and exchange connectivity, working with quantitative researchers and traders.

You will work with Linux, AWS, Docker, Kubernetes, CI/CD pipelines, Python, Bash, and monitoring tools like Datadog, Prometheus, CloudWatch, or ELK. You will support and maintain the critical research and trading infrastructure for a quantitative hedge fund, acting as first-line support for platform-related issues.

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.

Engineers design and optimize a real-time crypto execution platform using C++ and Rust, with tools like perf and VTune.

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, 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.

Build and own the cloud infrastructure for live AI-driven trading systems, using AWS, Kubernetes, Terraform, Python, Kafka, PostgreSQL, Redis, and ClickHouse. You will design deployment pipelines, multi-region environments, and observability with Grafana, Prometheus, and Loki. This is a founding engineering role, giving you direct ownership of the platform and architecture.

CMC Markets★3.8 (26)· London

Build scalable ETL/ELT pipelines using Python, SQL, Kafka, Spark, Parquet, Arrow, and modern data lake technologies. You will process billions of high-frequency market data records—trades, ticks, order books—to support quantitative research and production trading systems at CMC Markets.

Uses Jira and Confluence; desired familiarity with C++ or Python, Linux, and CI/CD. The Market Data Integration team manages market data projects, including technical scoping, migrations, and coordination with exchanges and vendors for live trading and historical research data.

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, 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.

Man Group★3.6 (14)· London

Lead development of Man Group’s core Python compute platform, working with Kubernetes, Spark, Hadoop Yarn, Ray, and Airflow on Linux for systematic and discretionary investment strategies. You will build the research platform and APIs for researchers and developers.

BlackRock★3.8 (632)· London

Build and maintain equity risk models using Python within the Aladdin Financial Engineering team, which develops financial models for risk management analytics used by BlackRock and its clients. This role also contributes to applying AI and automation to modernize workflows.

Will work with KDB+/q, real-time data ingestion, historical databases, and Linux/Unix scripting to build platforms for Commodities and Global Markets, supporting Quantitative Investment Strategies with market data for trading, risk, and research across multi-asset products including commodities, options, futures, and indices. Joins a small, hands-on team modernising the KDB ecosystem with cloud migration and AI.

KDB+/q, Python, and Linux/Unix. Builds and optimises real-time data platforms supporting systematic trading, quantitative research, and investment decision-making for a global investment firm.

You will use modern C++ and Linux to develop and optimize low-latency trading infrastructure and distributed systems. The team builds market data, order entry, and execution systems for global financial markets, supporting real-time trading and quantitative research. You collaborate closely with traders and researchers.

Jump Trading★3.8 (11)· London

A 10-week internship working with Python on Linux, building and maintaining data pipelines and infrastructure for Jump Trading's proprietary trading platform, using Agile development and production code.

Jump Trading★3.8 (11)· London

Systems Engineers at Jump Trading work with Linux/UNIX, Python, and networking technologies (routing, multicast, ethernet) to build automation and optimize infrastructure. They monitor, analyze, and troubleshoot the trading platform, which supports Jump’s high-frequency trading operations in global data centers. This is an individual contributor role without a stated team size.