Quantitative Developer

An established, globally recognised investment firm is seeking an experienced Quantitative Developer with a focus on Machine Learning to join a collaborative and entrepreneurial investment team.

The successful candidate will develop and deploy machine learning models using high-frequency financial data, while building and enhancing the research and computational infrastructure that supports them.

Working closely with senior investment professionals, you will translate advanced machine learning research into production-ready models and signals. The role will also involve enhancing the wider research infrastructure, including distributed computation, large-scale parameter optimisation, and efficient workflows from research through to production.

• Design, train and productionise large-scale machine learning models, spanning both classical and deep learning approaches.
• Enhance and optimise end-to-end machine learning pipelines, from large-scale data processing and distributed computation to scalable parameter search and model validation.
• Improve the speed, scalability and reliability of research and signal development environments, ensuring efficient migration from research into production.
• Collaborate with broader technology teams to leverage shared internal platforms, tools and services effectively.

Qualifications

• Master's degree, PhD or postdoctoral qualification in Computer Science, Mathematics, Statistics, Engineering, Physics or a related quantitative discipline from a leading academic institution.

Preferred Technical Skills

• Experience developing and validating machine learning models on large, complex datasets, covering both classical and deep learning approaches, in industry or academia.
• Experience building distributed computing systems for machine learning applications.
• Strong Python programming skills beyond the standard research stack, including parallelism, distributed computing and native acceleration (e.g. Python or C++ bindings).
• Familiarity with C++ is a strong advantage, alongside the software engineering fundamentals to develop proficiency quickly.
• Experience building agentic AI systems, including tool use, orchestration and evaluation.
• Experience with backtesting and an understanding of common quantitative research pitfalls, including overfitting, lookahead bias and survivorship bias.
• Understanding of systematic investment strategies and quantitative research workflows.
• Experience supporting production research workflows or model deployment within a front-office environment.