Python, C++, KDB+/q, Git, Agile, CI/CD, and database technologies. You will partner with Quantitative Researchers to build scalable Python applications and high-performance data processing for research, analytics, and live trading at a hedge fund.

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.

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.

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.

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.

Work with Terraform, Ansible, Kubernetes, Python, Bash, Go, Jenkins, GitHub Actions, Prometheus, and Grafana to build infrastructure automation platforms supporting python-based quantitative research and trading systems. Manage on-premises resources across Ubuntu and Windows Server environments.

Work with C++, Python, Unix, and git in a quant fund's crypto division. You will build and optimize trading strategies, research new protocols and data sources, and design and improve research and trading systems.

Builds cloud platforms for a systematic quant hedge fund's trading and research, using AWS (EC2, S3, VPC), Python/Bash, Jenkins/GitLab CI, Terraform/CloudFormation/AWS CDK, Docker, and Kubernetes to support digital asset and crypto systems.

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'll use Python, relational databases, data pipelines, and optionally cloud (AWS/GCP), containers, CI/CD, and TDD. The team builds a scalable trading and backtesting platform for quant researchers and portfolio managers, handling market and alternative data for simulation and live execution.

Python, C++ and AWS with DevOps/CI/CD for real-time systems. You’ll build and enhance the quant tech stack for systematic crypto trading, integrating front-office systems and on-chain DeFi data feeds. You’ll also mentor junior developers.

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.

You will use low-level C++, automated testing, and CI/CD to design and develop low latency trading components and infrastructure for the Systematic technology team at Balyasny Asset Management L.P., a global asset manager.

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.

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.

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

Work with Linux/UNIX, shell scripts, Python, and networking concepts like routing, multicast, and ethernet. You will join Trading Infrastructure to architect, build, and maintain low-latency trading platforms, including colo design and exchange connectivity.

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.