Our client is a leading and well-established High-Frequency Prop Trading and Market-Making firm. The business is going from strength to strength and enjoys exceptional profits. As a consequence, the need has arisen to hire an additional senior software engineer with an accomplished background working in low-latency trading systems, commercial experience with Rust and a background in either C++ or Java with a focus on optimising performance.

You would work on a bleeding-edge tech estate with a team of elite engineers who operate in an inclusive and collaborative culture. Our client seeks the best and offers an above-market basic salary and exceptional bonuses.

THE ROLE:

• Create efficient, low-latency trading systems in Rust, replacing the current blend of Java and C++ where necessary
• Collaborate closely with your colleagues in quantitative research, and trading to understand business needs and devise cutting-edge technical solutions.
• Compose clean, maintainable, and optimized code with a strong focus on minimizing latency, maximizing throughput, and enabling seamless scalability.
• Establish and manage a sturdy trading infrastructure capable of effortlessly managing substantial data and transaction loads.
• Work in tandem with other developers, DevOps specialists, and IT support teams to ensure the smooth and error-free operation of our trading systems.

SKILLS / EXPERIENCE REQUIRED:

• Proficiency in Rust with a background in either Java and C++.
• Exceptional software engineering skills including multithreading, concurrency, and networking
• Knowledge of financial markets and trading systems.
• Adeptness with various communication protocols, including TCP, UDP (unicast and multicast), as well as a deep understanding of WebSocket, HTTP, and FIX protocols.
• Exposure to SBE (Simple Binary Encoding) or related protocols
• Practical experience in Inter-process Communication (IPC) over shared memory.
• Strong problem-solving and analytical abilities.
• Exceptional communication and collaboration skills.
• Awareness of time-series databases and big data technologies.