Quantitative Developer, Research & ML Engineering, Systematic Macro

Millennium is a top tier global hedge fund with a strong commitment to leveraging market innovations in technology and data to deliver high-quality returns.

Job Description

A collaborative and entrepreneurial systematic macro pod is seeking an experienced Quantitative Developer with a machine learning focus. You will develop and deploy machine learning models on high-frequency market data, and build the research and compute infrastructure behind them.

The successful candidate will develop, optimize, and deploy machine learning models - classical and deep learning - applied to high-frequency market data within the systematic pod, working closely with the Senior Portfolio Manager to turn models into live trading signals. The role also extends to enhancing the pod’s wider research infrastructure: distributed computation, large-scale parameter search, and a streamlined path from research to production.

Location

London

Principal 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 the pod’s 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 the pod’s 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

Qualifications

• Master's or PhD/Post doctorate in Computer Science, Mathematics, Statistics, Engineering, Physics, or a related quantitative discipline, from a leading institution

Preferred Technical Skills

• 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 - 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 - tool use, orchestration, and evaluation

High Valued Experience

• 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