ML Engineer: Systematic Macro & Quantitative Research

Millennium

Greater London

On-site

GBP 120,000 - 180,000

Full time

14 days+
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Job summary

Millennium in London is seeking an experienced Quantitative Developer focused on machine learning to turn models into live trading signals. You will develop and deploy ML models on high-frequency market data and build the research and compute infrastructure behind them.

You will design, train, and productionize large-scale ML systems, optimize the end-to-end pipeline, and collaborate with technology teams to leverage internal platforms.

Qualifications

  • Master's or PhD in Computer Science, Mathematics, Statistics, Engineering, Physics, or a related quantitative discipline.
  • 3+ years of professional experience in software engineering or quantitative development.
  • Experience building distributed computing systems for ML applications.
  • Strong Python programming skills beyond the standard research stack.
  • Familiarity with C++ is a strong plus.

Responsibilities

  • Design, train and productionize large-scale ML models across classical and deep learning approaches for high-frequency data.
  • Enhance and optimize the ML pipeline, from data processing to scalable parameter search and validation.
  • Improve speed, scalability, and reliability of the signal development environment, ensuring smooth path from research to production.
  • Collaborate with technology teams to leverage shared internal platforms and services.

Skills

Python
Distributed compute
Linux
C++ bindings
Machine learning

Education

Master's or PhD

Tools

C++

Job description

Millennium in London is seeking an experienced Quantitative Developer focused on machine learning to turn models into live trading signals. You will develop and deploy ML models on high-frequency market data and build the research and compute infrastructure behind them.

You will design, train, and productionize large-scale ML systems, optimize the end-to-end pipeline, and collaborate with technology teams to leverage internal platforms.

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