Quantitative ML Engineer for Systematic Macro Research

United States Digital Space LLC; Greater London; On-site; GBP 90,000 - 150,000; Full time

Job summary

Millennium, a leading global hedge fund, seeks an experienced Quantitative Developer with a focus on machine learning to design and deploy ML models for high-frequency market data. You will also build and optimize the research and compute infrastructure that supports scalable model development and production use.

Responsibilities include training and productionizing models, building distributed pipelines, and collaborating with technology teams to leverage shared platforms.

Qualifications

• Master’s or PhD/Post doctorate in Computer Science, Mathematics, Statistics, Engineering, Physics, or a related quantitative discipline, from a leading institution

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

Skills

Python programming

Linux development

Distributed computing

Machine learning

C++ bindings

Education

Master’s or PhD in a quantitative field

Tools

Python

C++

Distributed computing

Linux

Job description

Millennium, a leading global hedge fund, seeks an experienced Quantitative Developer with a focus on machine learning to design and deploy ML models for high-frequency market data. You will also build and optimize the research and compute infrastructure that supports scalable model development and production use.

Responsibilities include training and productionizing models, building distributed pipelines, and collaborating with technology teams to leverage shared platforms.