Quantitative Developer, Research & ML Engineering, Systematic Macro

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.

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