Description

About the Firm

Our client is a leading global quantitative hedge fund that applies advanced research, technology, and data-driven methods to investment problems across international financial markets. The firm is expanding its AI research business and is looking for an exceptional AI Researcher to develop novel machine-learning approaches for quantitative investment research. This is an opportunity to work with large-scale datasets, advanced computing resources, and experienced researchers and engineers in a highly collaborative environment.

Key Responsibilities

• Research and develop advanced machine-learning and deep-learning methods for quantitative investment applications.
• Design predictive models using large-scale financial, market, and alternative datasets.
• Explore areas including time-series modelling, representation learning, transformers, large language models, reinforcement learning, generative AI, and foundation models.
• Develop robust approaches to feature learning, signal discovery, forecasting, and model combination.
• Design experiments and evaluate models with a strong focus on robustness, generalisation, interpretability, and computational efficiency.
• Adapt and implement ideas from leading academic research, identifying techniques with potential applications in financial markets.
• Work closely with quantitative researchers, portfolio managers, data scientists, and engineers to bring research ideas into production.
• Improve research tools, machine-learning pipelines, and large-scale model-training frameworks.
• Present research findings clearly and contribute to the team's broader AI research direction.

Qualifications

• Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, Physics, Engineering, or another highly quantitative discipline.
• Strong theoretical and practical understanding of machine learning, deep learning, statistics, and optimisation.
• Demonstrated expertise in at least one relevant area, such as:
  • Time-series modelling
  • Representation learning
  • Natural language processing or large language models
  • Transformers and foundation models
  • Reinforcement learning
  • Generative modelling
  • Probabilistic modelling
  • Computer vision or multimodal learning
• Excellent programming skills in Python and hands-on experience with frameworks such as PyTorch, JAX, or TensorFlow.
• Ability to independently formulate research questions, design experiments, and evaluate results rigorously.
• Experience working with large datasets and GPU-based or distributed model-training environments.
• Strong communication skills and the ability to collaborate across research and engineering teams.
• Intellectual curiosity, creativity, and a genuine interest in applying AI to challenging real-world problems.