Quantitative Developer

Company Description Rayleigh Research Oy specializes in automated high-frequency trading algorithm research and development, focusing on cutting-edge quantitative methods and robust technology. The company builds realistic L3 MBO limit-order-book simulations, giving teams a highly accurate environment for testing strategies and models. This simulation framework enables development and deployment of advanced AI-driven trading approaches in a controlled, research-oriented setting. Professionals joining Rayleigh Research Oy collaborate at the intersection of quantitative finance, software engineering, and machine learning, contributing directly to algorithmic trading innovations. The organization values rigorous experimentation, performance optimization, and practical applications of research in live markets.

Role Description As a Quantitative Developer at Rayleigh Research Oy, you design, implement, and optimize high-frequency trading algorithms and supporting infrastructure in a full-time capacity. In this hybrid role based in Helsinki, you work partly onsite and partly from home, collaborating with quantitative researchers to translate trading ideas into robust, production-ready code. Day-to-day tasks include building and maintaining limit-order-book simulation frameworks, implementing low-latency data processing pipelines, and integrating models into automated trading systems. You will profile and optimize code for performance, ensure reliability and correctness through testing, and contribute to tooling for research, backtesting, and monitoring. The role also involves reviewing peers’ code, documenting systems and workflows, and staying current with advancements in quantitative finance, AI, and software engineering.

Qualifications

• Strong programming skills in languages commonly used in quantitative finance (e.g., C++, Python, or similar), with experience writing efficient, production-quality code.
• Solid background in algorithms, data structures, and numerical computing, including performance optimization and memory-efficient implementations.
• Experience with quantitative methods or financial market microstructure, especially limit-order-book modeling, backtesting, and simulation-based strategy evaluation.
• Familiarity with machine learning or AI techniques applied to time-series, trading signals, or predictive modeling is highly beneficial.
• Comfort working with Linux-based development environments, version control (e.g., Git), and automated testing and deployment workflows.
• Ability to analyze complex systems, communicate technical concepts clearly, and collaborate effectively in a research-driven, hybrid workplace.
• Relevant academic background in computer science, mathematics, physics, engineering, quantitative finance, or a related field; advanced degrees are an advantage but not required.
• Prior experience in high-frequency or systematic trading, or in building real-time, low-latency systems, is considered a strong plus.