Quantitative Developer

Quantitative Developer

Industry: Systematic Trading / Quantitative Research

About the Company

A trading firm operates across global markets, combining advanced quantitative research, high-performance engineering, and deep market expertise to develop systematic strategies across multiple asset classes. The culture is highly collaborative, research-focused, and grounded in technical excellence.

Role Overview

The firm is seeking a talented Quantitative Developer to join its front-office engineering and research team. In this role, the developer will build and optimise the core systems that drive trading strategies, working closely with quants, traders, and data engineers. The position involves designing high-performance tools, developing production-ready models, and enabling robust research workflows at scale.

Responsibilities

• Develop and maintain low-latency, high-throughput systems supporting systematic trading.
• Build tools and libraries for model research, backtesting, and live deployment.
• Collaborate with quantitative researchers to implement production-grade versions of trading signals and strategies.
• Design efficient data pipelines for ingesting, cleaning, and transforming large-scale market datasets.
• Optimise performance of existing research and execution frameworks.
• Contribute to architecture decisions around compute, data, and model-serving infrastructure.

Requirements

• Strong programming skills in Python and/or C++ (experience in high-performance environments is a plus).
• Solid understanding of data structures, algorithms, and software engineering best practices.
• Experience working with financial markets, quantitative research, or data-intensive systems.
• Knowledge of time-series data, market microstructure, or systematic trading workflows is advantageous.
• Ability to work closely with researchers and translate ideas into robust, scalable code.
• Proactive mindset and high attention to quality, performance, and reliability.

Nice to Have

• Familiarity with distributed computing, modern data platforms, or cloud-native tooling.
• Experience with backtesting frameworks or real-time trading systems.
• Exposure to ML workflows or model training pipelines.