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Job Descrption

We are in search of a Quantitative Developer to join our team who is passionate about designing, architecting, and implementing low latency C++ systems that are not only robust, resilient, and accurate, but also exceptionally fast. Our team works directly with the firm’s central trading teams. By constructing and maintaining this high-performance infrastructure used by these teams, this developer will enable new trading opportunities across businesses and regions, allowing the best possible execution performance.

Job Duties
• Development of execution algorithms, order management systems, strategy containers, connectivity, and messaging systems.
• Work directly with central trading teams to optimize the firm’s overall execution performance.
• Enhance the platform's efficiency by utilizing network and systems programming, along with other advanced techniques to reduce latency.
• Create systems, interfaces, and tools for historical market data and trading simulations to boost research... productivity and system testability.
• Assist in building and maintaining our automated tests, performance benchmark framework, and other tools
• Collaborate closely with trading teams to gather requirements and develop solutions in a fast-paced environment

Qualifications
• 5+ years of professional experience in a front-office, financial services environment as a senior contributor
• 10+ years cumulative, professional experience
• Strong background in data structures, algorithms, and object-oriented programming in C++, including:
• Proficiency with new features of C++17 and C++20
• Proficiency with multithreading and asynchronous environments
• Strong understanding of low-latency and real-time system design and implementation
• Strong understanding of Linux system internals and networking
• Strong financial experience across multiple asset classes, with a focus on real-time low-latency trading systems for equities and futures
• Familiarity with python for quantitative research and data-oriented processing
• Familiarity with analysis of execution algorithm performance

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