Machine Learning Engineer - Quantitative Trading Firm - London

This is a remote position.

We’re seeking a highly skilled Machine Learning Engineer to join a dynamic team at a leading quantitative trading firm known for leveraging technology to drive global trading strategies. This role offers a unique opportunity to work at the intersection of cutting-edge machine learning research and high-performance trading systems.

As part of the ML team, you’ll be responsible for developing, deploying, and optimizing machine learning models that enhance decision-making across multiple asset classes. You will collaborate closely with researchers, data scientists, and software engineers to build robust, scalable ML infrastructure that supports rapid experimentation and production-level performance.

The ideal candidate combines a strong theoretical understanding of machine learning with hands-on experience in building end-to-end systems. You’ll apply your expertise in various ML techniques—ranging from deep learning and gradient-boosted trees to ensemble methods—to solve complex problems in a fast-paced, data-rich environment.

You will also focus on refining research workflows, improving model reproducibility, and ensuring that models integrate smoothly with trading infrastructure.

• Design, build, and maintain scalable training and inference pipelines for ML models used in trading decisions
  

• Collaborate across teams to translate research innovations into production-ready systems
  

• Optimize algorithms and model architectures to maximize predictive accuracy and latency requirements
  

• Contribute to the continuous improvement of tools and processes that accelerate ML research cycles
  

• Analyze large, complex datasets to extract insights and support data-driven decision-making
  

Requirements

• A strong background in machine learning, statistics, or a related quantitative field is essential.
  
• Experience with ML frameworks such a PyTorch or Tensorflow
  
• Proficiency in Python and/or C++ for high-performance computing is required.
  
• Candidates should have a solid foundation in mathematics, including linear algebra, optimization, and probability theory.
  
• Familiarity with building reproducible research pipelines and managing codebases collaboratively is important.
  
• Comfort working in Linux environments and cloud or distributed computing platforms is expected.