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About the Role

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

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

Requirements

• Master’s 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 — 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
• 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

Skills

• Machine Learning
• Python
• C++
• Linux
• Distributed Computing
• Data Processing
• Backtesting
• Market Microstructure

Location

• London

Work Type

• Full-time

Experience Level

• 3+ years

Education Level

• Master's or PhD/Post doctorate

About the Company

• 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.