Machine Learning Quantitative Researcher

About the Role We are partnering with a leading quantitative hedge fund that is continuing to invest heavily in its machine learning research capabilities. The firm is seeking an exceptional Machine Learning Quantitative Researcher to develop cutting-edge predictive models that drive systematic investment strategies across global financial markets. Working within a highly collaborative team of quantitative researchers, machine learning scientists, and engineers, you will leverage modern deep learning techniques to extract signals from large, complex datasets and translate research into production-ready trading models. The role offers significant autonomy, world-class infrastructure, and the opportunity to solve challenging real-world machine learning problems at scale. Responsibilities Conduct original machine learning research to identify and develop predictive signals for systematic trading. Design, train, and evaluate state-of-the-art deep learning models across structured, time-series, and alternative datasets. Develop novel approaches using transformers, sequence models, graph neural networks, representation learning, self-supervised learning, and reinforcement learning where appropriate. Build robust research pipelines for large-scale experimentation, model validation, and performance analysis. Work closely with quantitative researchers and engineers to transition research models into production. Improve model performance through feature engineering, hyperparameter optimization, and scalable training techniques. Stay at the forefront of advances in machine learning and apply emerging research to financial markets. Requirements PhD or Master's degree in Machine Learning, Computer Science, Artificial Intelligence, Mathematics, Statistics, Physics, Engineering, or a related quantitative discipline. Strong background in modern machine learning and deep learning methodologies. Excellent programming skills in Python, with experience using machine learning frameworks such as PyTorch or TensorFlow. Experience working with large-scale datasets and distributed model training. Strong understanding of statistical modelling, optimization, and experimental design. Demonstrated research ability through publications, industry research, or significant machine learning projects. Excellent problem-solving skills and the ability to communicate complex technical concepts clearly. Preferred Experience Experience with transformers, large language models, representation learning, or reinforcement learning. Knowledge of time-series modelling and sequential prediction. Familiarity with GPU computing and distributed training environments. Previous experience applying machine learning research within quantitative finance is beneficial but not required. What the Firm Offers Opportunity to work alongside some of the industry's leading quantitative researchers and machine learning scientists. Access to exceptional compute infrastructure and proprietary datasets. Highly collaborative, research-driven environment with significant intellectual freedom. Direct impact on live systematic investment strategies. Market-leading compensation, discretionary bonus, and long-term career progression. #J-18808-Ljbffr