Quantitative ML Research Engineer

Millennium Management; Greater London; On-site; GBP 36,000 - 76,000; Full time

Job summary

Millennium Management in London is seeking a skilled ML research engineer to design, train, and productionize large-scale models that operate on high-frequency market data.

You will optimize end-to-end ML pipelines—from data processing and distributed computation to scalable parameter search and model deployment.

Collaborating with the Senior Portfolio Manager and technology teams, you will turn research into live trading signals and strengthen our research infrastructure.

Qualifications

• Masters or PhD in Computer Science, Mathematics, Statistics, Engineering, Physics, or a related quantitative discipline.
• 3+ years of professional software/quantitative development experience.
• Experience building ML models on large, complex datasets.
• Experience building distributed ML systems.
• Strong Python programming beyond the research stack, including parallelism.
• Knowledge of C++ is a strong plus.
• Experience with production research workflows or model deployment.
• Familiarity with Linux development.

Responsibilities

• Design, train, and productionize large-scale ML models for high-frequency data.
• Enhance the ML pipeline from data processing to scalable validation.
• Improve speed, scalability, and reliability of the research environment.
• Partner with technology teams to use shared internal platforms.

Skills

Python

Linux

Machine Learning

C++

Distributed computing

Backtesting awareness

Education

Master's or PhD in a quantitative field

Job description

Millennium Management in London is seeking a skilled ML research engineer to design, train, and productionize large-scale models that operate on high-frequency market data.

You will optimize end-to-end ML pipelines—from data processing and distributed computation to scalable parameter search and model deployment.

Collaborating with the Senior Portfolio Manager and technology teams, you will turn research into live trading signals and strengthen our research infrastructure.