Quantitative Developer (C++)
Role Overview
Join an established market-making firm's Market Making team to build trading algorithms central to the business.
Own algorithm development from research tooling and back-testing through optimized, production-grade C++ running in live markets.
Work closely with traders and engineers and contribute ideas while developing knowledge of market microstructure.
Key Skills for This Role
Full Job Posting
About the role
Join an established market-making firm's Market Making team to build trading algorithms central to the business.
Own algorithm development from research tooling and back-testing through optimized, production-grade C++ running in live markets.
Work closely with traders and engineers and contribute ideas while developing knowledge of market microstructure.
Responsibilities
- Design, develop, and deploy trading algorithms and supporting research workflows and tools.
- Build and run back-testing frameworks to take strategies from idea to live trading.
- Create scalable, standardized solutions in partnership with the trading team.
- Write high-performance, low-latency C++ and drive extensive testing and optimization to production standards.
- Uphold engineering best practices across the software development lifecycle, including code review, testing, CI/CD, and documentation.
- Improve compute capability through continual infrastructure development.
Essential requirements
- 3+ years in a development role within capital markets.
- Strong modern C++ (C++17/20), focused on performance and production-quality code.
- Hands-on experience with order books and market-depth data.
- Solid version-control and CI/CD experience with Git, Azure DevOps, or similar.
- Rigorous approach to unit and integration testing, such as GoogleTest or Catch2.
- BSc/MSc in Computer Science, Mathematics, or a related field.
- Fluent English; curious, autonomous, and able to solve problems while contributing to a team.
Desirable experience
- Exposure to high-frequency or market-making trading strategies.
- Python for research, data analysis, or tooling.
- Background in machine learning, data science, or data engineering.
- Familiarity with low-latency techniques, including memory management, concurrency, profiling, and Linux performance tuning.
Why apply
- Direct impact on P&L, with code running in live markets alongside traders.
- End-to-end ownership from research and back-testing through deployment.
- Collaborative, flat team where ideas can move quickly into production.
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