Quantitative Developer, Research & ML Engineering

Millennium Management; Greater London; On-site; GBP 36,000 - 76,000; Full time

Job summary

Millennium Management in London is seeking a skilled ML research engineer to design, train, and productionize large-scale models that operate on high-frequency market data.

You will optimize end-to-end ML pipelines—from data processing and distributed computation to scalable parameter search and model deployment.

Collaborating with the Senior Portfolio Manager and technology teams, you will turn research into live trading signals and strengthen our research infrastructure.

Qualifications

• Masters or PhD in Computer Science, Mathematics, Statistics, Engineering, Physics, or a related quantitative discipline.
• 3+ years of professional software/quantitative development experience.
• Experience building ML models on large, complex datasets.
• Experience building distributed ML systems.
• Strong Python programming beyond the research stack, including parallelism.
• Knowledge of C++ is a strong plus.
• Experience with production research workflows or model deployment.
• Familiarity with Linux development.

Responsibilities

• Design, train, and productionize large-scale ML models for high-frequency data.
• Enhance the ML pipeline from data processing to scalable validation.
• Improve speed, scalability, and reliability of the research environment.
• Partner with technology teams to use shared internal platforms.

Skills

Python

Linux

Machine Learning

C++

Distributed computing

Backtesting awareness

Education

Master's or PhD in a quantitative field

Job description

Salary: £36,000 - 76,000 per year

Requirements

• Masters or PhD/Post doctorate in Computer Science, Mathematics, Statistics, Engineering, Physics, or a related quantitative discipline, from a leading institution
• 3+ years of professional experience in software engineering, quantitative development, or a related computational role
• Experience developing and validating machine learning models on large, complex datasets, across both classical and deep learning approaches, in industry or academia
• Experience building distributed computing systems for machine learning applications
• Strong Python programming skills beyond the standard research stack, including parallelism, distributed compute, and native acceleration such as Python or C++ bindings
• Familiarity with C++ is a strong plus, alongside the software engineering fundamentals to pick it up quickly
• Experience building data-intensive tools, research workflows, or model development infrastructure
• Strong Linux development experience
• Experience building agentic AI systems, including tool use, orchestration, and evaluation
• Experience with backtesting and awareness of common research pitfalls such as overfitting, lookahead bias, and survivorship bias
• Understanding of systematic trading strategies and quantitative research workflows
• Knowledge of market microstructure
• Experience supporting production research workflows or model deployment in a front-office environment

Responsibilities

• Design, train, and productionize large-scale machine learning models across both classical and deep learning approaches, applied to high-frequency data
• Enhance and optimize our end-to-end machine learning pipeline, from large-scale data processing and distributed computation to scalable parameter search and validation
• Contribute to improving the speed, scalability, and reliability of our wider signal development environment, ensuring consistent and efficient migration from research to production
• Partner with broader technology teams to make effective use of shared internal platforms and services

Technologies

• Agentic AI
• AI
• Linux
• Machine Learning
• Python

We are a top-tier global hedge fund with a strong commitment to leveraging market innovations in technology and data to deliver high-quality returns. We are hiring for a collaborative and entrepreneurial systematic macro pod based in London, where the role focuses on machine learning research engineering, high-frequency market data, and building the research and compute infrastructure behind our models. The position works closely with our Senior Portfolio Manager to turn models into live trading signals.