Why This Role Stands Out
This hybrid role offers an exceptional opportunity to apply cutting-edge AI and deep learning techniques to solve complex financial prediction problems, directly impacting investment performance within a leading global platform. You'll thrive here if you possess a strong background in machine learning research and are eager to collaborate with a successful systematic investment team dedicated to innovation.
Quick Overview
Job Description
Join one of the world's leading multi-strategy investment platforms as a Quant Researcher focused on Machine Learning and AI. This role sits within a highly successful systematic investment team that is investing heavily in next-generation research capabilities and the application of cutting-edge machine learning techniques to financial markets.
The team is seeking exceptional researchers who can bring expertise from frontier AI, deep learning, large-scale modelling, and modern machine learning research into a fast-paced environment where ideas are rapidly tested and deployed. This is an opportunity to work on some of the most challenging prediction problems in the world while having direct impact on investment performance.
Responsibilities
- Develop and implement machine learning models to identify predictive signals across global markets
- Research and apply state-of-the-art deep learning techniques for alpha generation
- Design and test neural network architectures for financial forecasting and pattern recognition
- Work with large, complex, and alternative datasets to uncover unique sources of investment insight
- Conduct rigorous research, experimentation, and statistical validation of trading signals
- Collaborate closely with Portfolio Managers, Quant Researchers, and Data Scientists
- Build scalable research infrastructure and production-ready modelling pipelines
- Stay at the forefront of developments in AI, machine learning, and quantitative research
- Contribute to the development of systematic investment strategies using modern ML techniques
Requirements
- PhD or Master's degree in Machine Learning, Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative discipline
- Strong background in machine learning research and modelling
- Experience building and deploying deep learning models in real-world environments
- Expertise in areas such as neural networks, representation learning, transformers, foundation models, reinforcement learning, probabilistic modelling, or large-scale optimisation
- Excellent programming skills in Python and familiarity with modern ML frameworks such as PyTorch or TensorFlow
- Strong mathematical and statistical foundations
- Demonstrated ability to conduct independent research and solve complex problems
- Experience from leading AI labs, deep technology companies, research institutions, or high-performance machine learning environments is highly desirable
Preferred Backgrounds
We are particularly interested in candidates from:
- Leading AI research labs
- Frontier foundation model teams
- Deep learning and applied AI organisations
- Research-focused technology companies
- Quantitative research groups
- High-performance computing and data science environments
- Academic researchers with strong publication records in machine learning or artificial intelligence
What You'll Get
- Opportunity to apply cutting-edge AI research to real-world investment decisions
- Access to significant computational, data, and research resources
- Highly collaborative environment alongside world-class investors and researchers
- Competitive compensation package with substantial upside potential
- Direct impact on investment outcomes and business growth
- Relocation support to Dubai where applicable
- Exposure to one of the most intellectually challenging applications of machine learning
This position is ideal for researchers who are passionate about solving difficult prediction problems, building innovative machine learning systems, and applying frontier AI techniques in a highly competitive and rewarding environment.
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