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G-Research

Quantitative finance research applying rigorous methods

Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeLondon, UKIn-person

About the Job

Requirements

  • A postgraduate degree in machine learning or a related discipline, or commercial experience working on machine-learning models at scale.
  • A proven record of success in online data-science competitions such as Kaggle may also be considered.
  • Excellent machine-learning reasoning and communication skills.
  • Ability to develop custom machine-learning models when off-the-shelf methods do not work effectively on the data.
  • Ability to work independently and collaboratively in small teams on engineering challenges, particularly in machine learning and scientific computing.

Responsibilities

  • Implement ideas from recently published research papers.
  • Write custom libraries for efficiently training on petabytes of data.
  • Reduce model-training times by manually optimizing machine-learning operations.
  • Profile custom machine-learning architectures to identify performance bottlenecks.
  • Evaluate the latest hardware and software in the machine-learning ecosystem.
  • Design systems that scale, accelerate discovery, and support innovation across the firm’s research platform.

Desired Qualifications

  • Strong object-oriented programming skills and experience working with Python, PyTorch, and NumPy.
  • Experience in one or more advanced optimization methods, modern machine-learning techniques, high-performance computing, profiling, or model inference.

About the Company

G-Research

Quantitative finance research applying rigorous methods

G-Research applies scientific rigour to solve complex challenges in financial markets. It brings together world-class researchers and engineers to develop data-driven models and trading ideas, testing them through rigorous experimentation and robust infrastructure. Unlike many traditional asset managers, its culture emphasizes deep exploration, reproducibility, methodical execution, and long-term thinking. The goal is to generate alpha through disciplined, evidence-based research that informs investment decisions and advances understanding of financial markets.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
2001

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