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.

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