Quantitative Developer

We are a technology-driven quantitative trading firm operating across global financial markets. Our success is built on a combination of rigorous research, advanced engineering, and a collaborative culture that empowers talented individuals to solve complex problems at scale. With teams spanning North America, Europe, Asia, and India, we bring together researchers, software engineers, traders, and operational specialists who share a passion for innovation and continuous improvement.

As markets evolve, we remain focused on developing new strategies, expanding into emerging opportunities, and building the technology that enables us to stay at the forefront of systematic trading.

We are seeking a Quantitative Developer to help bridge the gap between research and live trading. In this role, you will work closely with researchers and trading teams to transform quantitative ideas into robust, production-ready systems. From developing research infrastructure to supporting execution, you will have direct ownership over the tools and processes that drive trading performance.

What You’ll Do

• Develop and enhance platforms that support the entire strategy lifecycle, from research and testing through deployment and monitoring
• Build realistic simulation and backtesting frameworks that capture market behavior, transaction costs, latency, and execution dynamics
• Create, manage, and optimize feature generation pipelines across multiple asset classes and market environments
• Maintain reliable signal and data workflows that connect research outputs to production trading systems
• Partner with researchers to evaluate and refine trading strategies while accounting for operational and market constraints
• Investigate and resolve issues across both research and production environments, ensuring system reliability and performance

What We’re Looking For

• 3+ years of experience developing software for quantitative research, systematic trading, or financial technology environments
• Strong Python development skills, including experience working with modern data analysis libraries such as pandas, Polars, or equivalent tools
• Solid understanding of statistics, probability, and time-series data analysis, with hands-on experience building or using simulation and backtesting systems
• Familiarity with machine learning techniques and their application within systematic trading workflows
• Exposure to low-latency or performance-sensitive systems is a plus
• Ability to collaborate effectively with both research and engineering teams while navigating the full development lifecycle