Open roleExternal
Quantitative Developer, Research & ML Engineering, Systematic Macro
- london, england, United Kingdom
- Job type not listed
- £90 - £150 Per Hour
Job Description
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