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Machine Learning Engineer - Quantitative Trading Firm - London, London

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Location:

London, United KingdomJob Category:

Other-EU work permit required:

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6Posted:

13.08.2025Expiry Date:

27.09.2025col-wideJob Description:

This is a remote position.Were 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, youll be responsible for developing, deploying, and optim izing 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. Youll apply your expertise in various ML techniquesranging from deep learning and gradient-boosted trees to ensemble methodsto 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.

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