Our client, one of the most prestigious hedge funds in the world, operates at the forefront of systematic trading, applying rigorous research and cutting‑edge technology to global markets. The London‑based commodities team combines deep domain expertise with advanced quantitative methods, using machine learning as a practical research tool rather than a theoretical exercise. The group is highly collaborative, with engineers and researchers working closely together to build robust systems that directly support research and trading decisions.
Responsibilities
- Work closely with quantitative researchers to support and enhance machine‑learning‑driven research workflows
- Design, build, and maintain scalable infrastructure used for data processing, experimentation, and model development
- Bridge exploratory research code and production‑quality systems, improving robustness and reproducibility
- Contribute to improving performance, reliability, and usability of ML and data pipelines
- Collaborate with engineers and researchers to evolve tools and platforms as research needs change
Requirements
- Strong software engineering fundamentals with experience building data‑intensive or ML‑enabled systems
- Practical exposure to machine learning workflows, tooling, or platforms in a real production environment
- Experience working in finance or another domain with complex data and high reliability requirements
- Comfortable operating close to research, with ambiguity and fast iteration
- Expert level proficiency in Python and knowledge of at least one common backend or data‑focused programming language (e.g. C++, Java, etc.)