A global trading firm trading ETFs, Equities, Futures, Commodities, Options, Bonds, and Currencies seeks a Machine Learning Research Engineer. Machine Learning is a critical pillar of the firm's technology. No specific tools or team size are stated.

You will use Python, SQL, and Streamlit on an AWS data platform to clean survey data, build statistical and machine learning models (including NLP), and design interactive dashboards. As a Data Scientist II on the Data Science Consulting team, you will deliver predictive analytics solutions that optimise clients’ marketing effectiveness. You will also manage client relationships and guide more junior team members.

You will work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, cloud data tools (ideally Azure), and large language models (LLMs) to build predictive models and identify alpha signals. You will join the data science team of a leading investment management company.

Design, build, and optimise high-performance trading systems and data-intensive infrastructure for a buy-side fund, working with object-oriented languages, low-latency distributed systems, and advanced tooling. You will collaborate with traders, researchers, and engineers to drive real-time decision-making and maintain competitive advantage through emerging technologies and machine learning techniques.

You will work with Python 3.10+, JavaScript/TypeScript, Go, RabbitMQ, Kafka, PostgreSQL, Redis, OpenTelemetry, Prometheus, Grafana, and Zabbix on Linux servers. You will join a team building and supporting systematic trading models for a sports betting company. You will collaborate with quantitative researchers, developers, and traders.

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.

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.

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.

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.

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.

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.

JPMorgan★3.9 (20k)· London

Lead projects on the Strategic Index Technology team, using Python, Java, C++, or C# to build and maintain the cross-markets index platform alongside Quantitative Research, Trading, and Sales.

You will work with Go and/or Python, plus Kubernetes, Docker, and cloud infrastructure. You'll support a systematic cryptocurrency trading team by maintaining live production trading and research systems, and helping to deploy strategies.

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.

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 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.