New York, NY | Leading Quantitative Hedge Fund
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.
