Millennium Management★3.8 (21)· London

Use Python, C++, and tools like Docker, Kubernetes, Ceph, MongoDB, Kafka, Numpy, Polars, Scikit-learn, and PyTorch to build research tooling and real-time event-driven systems for alpha discovery, signal computation, and trade execution within a small systematic trading team.

Use Python and SQL with connections to execution/order management systems. Build components for live trading and simulations of systematic strategies across equities, futures, and foreign exchange. Work within a small trading pod.

You’ll build and maintain tools and frameworks in Python for trading risk analysis and reporting, processing large volumes of market and trade data across fixed income products. You will work with databases, APIs, and microservices architecture alongside Fixed Income Risk Managers, Traders, and Researchers on the Risk team at a tech-driven quant hedge fund.

Work with Python, C++ (preferred), tree-based models, deep learning, and NLP to develop predictive features from HFT/intraday market data. As part of a multi-strategy hedge fund’s Equity/Futures team, you will design ML-driven alphas for cash equities and futures.

Use Python, C++, distributed computing, machine learning, and HPC to build tools for quant research and signal generation. This team constructs the infrastructure powering trading signal research, letting researchers and traders test and scale strategies across global markets.

Develop low-latency trading engines (nanosecond-scale) and analytics infrastructure using modern C++. Python is a significant plus. You will work directly on a trading desk alongside traders and researchers at a quant hedge fund, owning projects from idea to completion.

Expert C++ and Python developer with strong Linux skills will build data pipelines, prediction tools, and systematic trading infrastructure for an equities pod, using machine learning libraries and DevOps tooling (Google Cloud, Airflow, Influx DB, Grafana) to support large-scale quantitative research and intraday trading.

You will use modern C++ and Python while working within a trading desk at a systematic hedge fund, contributing to an ultra-low-latency trading engine, research platform handling terabytes of data daily, and cloud-based analytics infrastructure.

Develop and maintain in-house C++ pricing libraries and Python tools for automated trading systems, signal research, and execution/portfolio construction. Work with QuantLib, fixed income, and FX derivatives. Build multi-threaded servers and model frameworks, collaborating closely with traders and quantitative analysts.

At a leading systematic equity hedge fund, you will work with Python, Azure, Docker, Kubernetes, Airflow, GitHub Actions, and financial data platforms like Snowflake, Bloomberg, and Databricks. You'll build scalable engineering infrastructure, modernize the research platform, and develop ETL pipelines for quantitative research and production. The role involves direct collaboration with portfolio managers and researchers.

You will work with Python, Bash, and some C++ to build and support systems that interface with markets, brokers, and data providers. The market-access team manages global market data systems by coordinating with exchanges, vendors, traders, and internal engineering teams.

Work with Terraform, Ansible, Kubernetes, Prometheus, Grafana, Python, Bash, Go, Jenkins, and both Ubuntu and Windows Server. As a member of Man Group’s Platform Engineering team, you will build and maintain infrastructure automation and self-service platforms supporting the firm’s global quantitative research and trading systems.

Build and optimize crypto trading strategies using C++, Python, Unix, and git. You will research new protocols and data sources, and design and improve research and trading systems within a leading quant fund’s rapidly expanding crypto division.

You will work with AWS (EC2, S3, VPC), Python or Bash, CI/CD tools (Jenkins, GitLab CI), Terraform, CloudFormation or AWS CDK, Docker, and Kubernetes. This role is on the Cloud Infrastructure team, building cloud platforms to support trading and research for a tech-driven quant and systematic hedge fund trading digital asset and crypto-related systems. You will serve trading, research, and development teams.

You'll work with Python, SQL, AWS, cloud environments, database technologies, and source control to build scalable data solutions. The role supports a hedge fund's global trading and research environment, transforming raw data into trusted datasets for investment strategies and improving data platform reliability.

You will work with AWS, IAM, SCP, RCP, GuardDuty, Security Hub, Config, CloudTrail, KMS, Terraform or CloudFormation, and Python. You will secure and optimize AWS infrastructure for a global trading and investment firm’s high-performance research and trading platforms.

You will work with Linux, AWS, Datadog, Prometheus, CloudWatch, ELK, Docker, Kubernetes, CI/CD pipelines, and scripting in Python or Bash. As a member of a hedge fund’s platform team, you will support and maintain the critical research and trading infrastructure that underpins investment decisions.

Python, relational databases, data pipelines, AWS/GCP, CI/CD, and containerized environments. You will build components for a trading and research platform used by quant researchers and portfolio managers, handling market and alternative data for simulation and live execution.

You will work with Python and large datasets (SQL, advanced .NET a plus) to research predictive signals from order book dynamics, trade flow, and market microstructure. This proprietary trading firm designs and deploys systematic HFT and market-making strategies for cryptocurrency markets, trading exclusively with its own capital. As a senior quantitative researcher, you will own the complete research lifecycle from hypothesis through live production.

You will work with C++ and Python to develop quantitative investment models, analyze large datasets, and engineer robust systems for systematic trading strategies. The role is within a small, autonomous pod led by a Portfolio Manager.

You will use Python, KDB and SQL to build a Vol Alpha library for Portfolio Managers. You join a new Volatility Alpha Development team of engineers, quants and data scientists, working on equity derivatives modelling, vol surface fitting, backtesting and volatility trading strategies.

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.

The Quantitative Researcher will work in Python or C++, building signals, models, and trading strategies for global financial markets, plus designing research tools and data pipelines. The role is within a small London-based team of researchers and engineers focused on systematic trading and quantitative research infrastructure.

You’ll research and develop mathematical and statistical models for systematic trading, using Python and ideally C++, SQL, machine learning, and large-scale data analysis. You’ll collaborate with quantitative portfolio managers and engineers at a global quantitative investment firm, covering full research through live production across markets and asset classes.

You will work with Python (pandas/numpy), React/Angular for web frontends, SQL, and optionally the Python data science stack or cloud technologies. You will join a Commodities Rapid Application Development team building end-to-end data, research, and trading platform solutions for Portfolio Managers.