Quantitative Developer, Research & ML Engineering, Systematic Macro
Skills
About the role
Quantitative Developer, Research & ML Engineering, Systematic Macro
Please direct all resume submissions to QuantTalentUS@mlp.com and reference REQ-30266 in the subject line.
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
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