Machine Learning Engineer - Quantitative Trading Firm - London

Marcus Knightley; London; Remote; GBP 70,000 - 90,000; Full time

Job summary

A leading quantitative trading firm in London is looking for a Machine Learning Engineer to develop and optimize models that enhance trading decisions. Candidates should have a strong background in machine learning and statistics, alongside proficiency in Python or C++. This remote position requires collaboration on scalable ML infrastructure in a fast-paced environment.

Qualifications

• Strong theoretical understanding of machine learning.
• Experience in building end-to-end ML systems.
• Proficiency in Python and/or C++.

Responsibilities

• Develop, deploy, and optimize machine learning models.
• Collaborate with teams to build scalable ML infrastructure.
• Analyze large datasets for insights.

Skills

Machine Learning

Statistics

Python

C++

Deep Learning

Linux

Education

Background in machine learning, statistics or a related field

Tools

PyTorch

TensorFlow

Job description

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Machine Learning Engineer - Quantitative Trading Firm - London, London

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Client:

Location:

London, United Kingdom

Job Category:

Other

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EU work permit required:

Yes

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Job Reference:

bb588acbaf74

Job Views:

6

Posted:

13.08.2025

Expiry Date:

27.09.2025

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Job Description:

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 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. 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.